Proceedings of the 29th International Conference on Computational Linguistics
Do Language Models Make Human-like Predictions about the Coreferents of Italian Anaphoric Zero Pronouns? (2022.coling-1)
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| Challenge: | Some languages allow arguments to be omitted in certain contexts, but human language comprehenders construct expectations about which referents are more likely. |
| Approach: | They ask whether Neural Language Models extract expectations from sentences with zero pronouns from five behavioral experiments conducted in italian by Carminati (2005). |
| Outcome: | The results suggest that human expectations about coreference can be derived from exposure to language, and also indicates features of language models that allow them to better reflect human behavior. |
Language Acquisition through Intention Reading and Pattern Finding (2022.coling-1)
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| Challenge: | a faithful computational operationalisation of the underlying mechanisms is still lacking. |
| Approach: | They propose a mechanistic model of intention reading and its integration with pattern finding capacities to model the intention reading process and their model of pattern finding. |
| Outcome: | The proposed model integrates intention reading and pattern finding processes with linguistic schemata that generalise over form and meaning. |
Stability of Syntactic Dialect Classification over Space and Time (2022.coling-1)
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| Challenge: | a paper examines the degree to which dialect classifiers remain stable over time . it finds that the models remain robust over time with a fixed decay rate . |
| Approach: | They construct a test set for 12 dialects of English that spans three years at monthly intervals with a fixed spatial distribution across 1,120 cities. |
| Outcome: | The proposed model can reveal linguistic variation over space and time. |
Subject Verb Agreement Error Patterns in Meaningless Sentences: Humans vs. BERT (2022.coling-1)
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| Challenge: | Existing research shows that humans are prone to making agreement errors with specific constructions. |
| Approach: | They compare the performance of BERT-base and that of humans using crowdsourcing . they find that meaningfulness is stronger for BERT than for humans . |
| Outcome: | The proposed model performs better than humans on a crowdsourcing experiment . |
Measuring Morphological Fusion Using Partial Information Decomposition (2022.coling-1)
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| Challenge: | agglutinative and fusional languages have a systematic relationship between meaning and form, but are less systematic when it comes to morphological relations. |
| Approach: | They propose a mathematically precise way of characterizing morphological systems using partial information decomposition. |
| Outcome: | The proposed framework decomposes mutual information into three components: unique, redundant, and synergistic information. |
Smells like Teen Spirit: An Exploration of Sensorial Style in Literary Genres (2022.coling-1)
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| Challenge: | Sensory perceptions shape how we use language and communicate. |
| Approach: | They examine the influence of sensorial language on writing style in a collection of lyrics, novels, and poetry. |
| Outcome: | The authors find that individual use of sensorial language is not a random phenomenon; choice is likely involved. |
Metaphorical Polysemy Detection: Conventional Metaphor Meets Word Sense Disambiguation (2022.coling-1)
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| Challenge: | Linguists distinguish between novel and conventional metaphors, a distinction which the metaphor detection task in NLP does not take into account. |
| Approach: | They propose a method which treats conventional metaphors as a property of word senses in a lexicon and combines metaphor detection with word sense disambiguation to train it. |
| Outcome: | The proposed model outperforms a state-of-the-art model in annotating metaphor in two subsets of WordNet and achieves .78 ROC-AUC score compared to baseline model . |
Machine Reading, Fast and Slow: When Do Models “Understand” Language? (2022.coling-1)
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| Challenge: | Existing models of reading comprehension score highly on NLU benchmarks, but they are often 'read fast', i.e. rely on shallow patterns. |
| Approach: | They propose a definition for the reasoning steps expected from a system that would be 'reading slowly' they compare that behavior with five models of the BERT family of various sizes, observed through saliency scores and counterfactual explanations. |
| Outcome: | The proposed model is compared with five models of the BERT family of various sizes, and compared using saliency scores and counterfactual explanations. |
Hierarchical Attention Network for Explainable Depression Detection on Twitter Aided by Metaphor Concept Mappings (2022.coling-1)
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| Challenge: | Existing black-box-like deep learning methods for depression detection focus on improving classification performance, but it is impossible to explain and interpret those models that rely on state-of-the-art (SOTA) deep learning techniques. |
| Approach: | They propose to use hierarchical attention mechanisms and feed-forward neural networks to encode a model for depression detection on Twitter that leverages metaphorical concept mappings as input. |
| Outcome: | The proposed model leverages metaphorical concept mappings as input to detect depressed individuals and identify features of such users’ tweets. |
Multi-view and Cross-view Brain Decoding (2022.coling-1)
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| Challenge: | a recent study has shown that brain decoding models can decode concepts from single view . a multi-view decoder can take brain recordings for any view as input and predict the concept . |
| Approach: | They propose to build a multi-view decoder that can take brain recordings for any view as input and predict the concept. |
| Outcome: | The proposed systems can decode concepts from brain recordings from any view . the proposed systems have 0.68 pairwise accuracy across view pairs and 0.8 average pairwise precision across tasks. |
Visio-Linguistic Brain Encoding (2022.coling-1)
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| Challenge: | Existing studies have failed to explore co-attentive multi-modal modeling for visual and text reasoning. |
| Approach: | They propose to use image and multi-modal Transformers to reconstruct fMRI brain activity . they use two popular datasets to study visual and text reasoning . |
| Outcome: | The proposed model outperforms existing models on two popular datasets . the results raise the question whether visual processing is affected implicitly by linguistic processing . |
Gestures Are Used Rationally: Information Theoretic Evidence from Neural Sequential Models (2022.coling-1)
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| Challenge: | Verbal communication is companied by rich non-verbal signals, but few studies have explored the non- verbal channels with finer theoretical lens. |
| Approach: | They extract gesture representations from monologue video data and train neural sequential models to examine their results. |
| Outcome: | The proposed method shows that speakers use simple gestures to convey information that enhances verbal communication. |
Revisiting Statistical Laws of Semantic Shift in Romance Cognates (2022.coling-1)
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| Challenge: | Despite their shared etymology, some cognate pairs have experienced semantic shift. |
| Approach: | They examine the relationship between lexical semantic shift and six intra-linguistic variables, such as frequency and polysemy, and examine the effect of morphologically complex etyma on semantic shift. |
| Outcome: | The results show that frequency and polysemy have positive effects on semantic shift and that morphologically complex etyma are more resistant to it. |
Character Jacobian: Modeling Chinese Character Meanings with Deep Learning Model (2022.coling-1)
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| Challenge: | Compounding is a prevalent word-formation process in Chinese morphology, where each character is bound and free when treated as a morpheme. |
| Approach: | They propose a model that learns non-linear relations between constituents and words and a character Jacobians model that describes character’s role in each word. |
| Outcome: | The proposed model predicts embeddings of real words from constituents but helps account for behavioral data of pseudowords. |
COMMA: Modeling Relationship among Motivations, Emotions and Actions in Language-based Human Activities (2022.coling-1)
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| Challenge: | Existing methods for modeling motivations, emotions and actions in language-based human activities have been limited. |
| Approach: | They propose to model motivations, emotions and actions in language-based human activities using a dataset called Story Commonsense. |
| Outcome: | The proposed model can better reveal the essential relationship between motivations, emotions and actions than existing methods. |
Exploring Semantic Spaces for Detecting Clustering and Switching in Verbal Fluency (2022.coling-1)
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| Challenge: | Existing evaluations of word/concept representations on verbal fluency tasks rely on human annotations of clusters and switches between sub-categories. |
| Approach: | They analyze word/concept representations in an experimental verbal fluency dataset . they find that ConceptNet embeddings outperforms other semantic representations . |
| Outcome: | The proposed method outperforms other semantic representations by a large margin. |
Neuro-Symbolic Visual Dialog (2022.coling-1)
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| Challenge: | Existing methods for visual dialog require large amounts of training data, which is prohibitive for most settings. |
| Approach: | They propose a method that integrates deep learning and symbolic program execution for multi-round visual reasoning. |
| Outcome: | The proposed model outperforms existing methods on long-distance co-reference resolution and vanishing question-answering performance. |
LINGUIST: Language Model Instruction Tuning to Generate Annotated Utterances for Intent Classification and Slot Tagging (2022.coling-1)
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| Challenge: | LINGUIST generates annotated data for Intent Classification and Slot Tagging (IC+ST) we demonstrate fine-tuning of a large-scale seq2seq model to control outputs of multilingual data generation. |
| Approach: | They propose a method for generating annotated data for Intent Classification and Slot Tagging (IC+ST) they use a 5-billion-parameter multilingual sequence-to-sequence model to fine-tune it on a flexible instruction prompt. |
| Outcome: | The proposed method outperforms state-of-the-art approaches on a SNIPS intent setting and shows significant improvement on IC+ST in a cross-lingual setting. |
Adaptive Natural Language Generation for Task-oriented Dialogue via Reinforcement Learning (2022.coling-1)
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| Challenge: | In task-oriented dialogue systems, the role of the natural language generation component is to convert a system's intentions, called dialogue acts (DAs), into natural language utterances and to convey DAs accurately to users. |
| Approach: | They propose a method for Adaptive Natural language generation for Task-Oriented dialogue via Reinforcement learning that incorporates a natural language understanding module into the objective function of RL. |
| Outcome: | The proposed method generates adaptive utterances against speech recognition errors and the different vocabulary levels of users in a multi-world task-oriented dialogue system. |
TAKE: Topic-shift Aware Knowledge sElection for Dialogue Generation (2022.coling-1)
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| Challenge: | Recent work finds that realizing who holds the initiative can help select knowledge . however, there is a strong semantic transition between two rounds, probably leading to initiative misjudgment . |
| Approach: | They propose a topic-shift Aware Knowledge sElector(TAKE) model which locates relevant parts from dialogue history to improve knowledge selection. |
| Outcome: | The proposed model outperforms baseline models on the WoW. |
Dynamic Dialogue Policy for Continual Reinforcement Learning (2022.coling-1)
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Christian Geishauser, Carel van Niekerk, Hsien-chin Lin, Nurul Lubis, Michael Heck, Shutong Feng, Milica Gašić
| Challenge: | Continual reinforcement learning of the dialogue policy has remained unaddressed . lack of a framework with training protocols, baseline models and suitable metrics has hindered research in this direction. |
| Approach: | They propose a continual learning algorithm, baseline architectures and metrics for assessing continual reinforcement learning models. |
| Outcome: | The proposed architecture can integrate new knowledge seamlessly and achieve significant zero-shot performance when exposed to unseen domains. |
GRAVL-BERT: Graphical Visual-Linguistic Representations for Multimodal Coreference Resolution (2022.coling-1)
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Danfeng Guo, Arpit Gupta, Sanchit Agarwal, Jiun-Yu Kao, Shuyang Gao, Arijit Biswas, Chien-Wei Lin, Tagyoung Chung, Mohit Bansal
| Challenge: | Multimodal coreference resolution (MCR) is a crucial capability for building next-generation conversational agents. |
| Approach: | They propose a multimodal coreference resolution model that resolves coreferences made in multi-turn dialogues with scene images. |
| Outcome: | The proposed model resolves coreferences made in multi-turn dialogues with scene images. |
Learning to Improve Persona Consistency in Multi-party Dialogue Generation via Text Knowledge Enhancement (2022.coling-1)
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| Challenge: | Existing methods suffer from incomprehensive persona tags that have unique and obscure meanings to describe human’s personality. |
| Approach: | They propose a graph convolution network model with addressee selecting mechanism that integrates personas, dialogue utterances, and external text knowledge in a unified graph. |
| Outcome: | The proposed model outperforms baselines by large margins and improves persona consistency in the generated responses. |
Improving Top-K Decoding for Non-Autoregressive Semantic Parsing via Intent Conditioning (2022.coling-1)
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| Challenge: | Semantic parsing (SP) is a core component of modern virtual assistants like Google Assistant and Amazon Alexa. |
| Approach: | They propose a non-autoregressive (NAR) semantic parser that introduces intent conditioning on the decoder. |
| Outcome: | The proposed model reduces inference latency while maintaining competitive parsing quality. |
Autoregressive Entity Generation for End-to-End Task-Oriented Dialog (2022.coling-1)
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| Challenge: | Task-oriented dialog systems require external knowledge base to generate a response . current systems require scanning the KB at each turn, which is inefficient when the kb scales up . |
| Approach: | They propose to generate entity autoregressively before leveraging it to guide response generation. |
| Outcome: | Experiments on MultiWOZ 2.1 single and CAMREST show that the proposed system generates more high-quality and entity-consistent responses in an end-to-end manner. |
Continual Few-shot Intent Detection (2022.coling-1)
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| Challenge: | Existing intent detection systems are trained with lots of labeled data over a predefined set of intent classes. |
| Approach: | They propose a prefix-guided lightweight encoder with three auxiliary strategies to prevent catastrophic forgetting and negative knowledge transfer across tasks. |
| Outcome: | The proposed system prevents catastrophic forgetting and encourages positive knowledge transfer across tasks. |
“Mama Always Had a Way of Explaining Things So I Could Understand”: A Dialogue Corpus for Learning to Construct Explanations (2022.coling-1)
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| Challenge: | a new corpus of dialogical explanations is created to help explainable AI . a linguistic analysis of 65 transcribed English dialogues shows that explanations are co-constructed in a dialogue between the explainer and the explainee . |
| Approach: | They propose a corpus of dialogical explanations that are co-constructed in a dialogue . they analyze linguistic patterns of explainers and explainees and explore differences . |
| Outcome: | The proposed corpus of dialogical explanations enables NLP research on how humans explain . the analysis shows that sequence information helps predicting topics, acts, and moves effectively . |
Schema Encoding for Transferable Dialogue State Tracking (2022.coling-1)
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| Challenge: | Recent work has focused on deep neural models for task-oriented dialogue systems . however, the neural models require a large dataset for training and a new dataset to be trained on another domain. |
| Approach: | They propose a schema encoder for transferable dialogue state tracking to new domains . they aim to transfer the model to new datasets by encoding new schemas based on the dataset . |
| Outcome: | The proposed method improves the accuracy of the proposed model on multi-domain settings. |
A Personalized Dialogue Generator with Implicit User Persona Detection (2022.coling-1)
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| Challenge: | Existing models for personalized dialogue generation tend to be self-centered, with little care for the user in the dialogue. |
| Approach: | They propose a personalized dialogue generator by detecting an implicit user persona and using conditional variational inference to model the user's potential persona with no external knowledge. |
| Outcome: | The proposed model improves both automatic metrics and human evaluations by focusing on the user's persona and posterior-discriminated regularization. |
Incorporating Causal Analysis into Diversified and Logical Response Generation (2022.coling-1)
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| Challenge: | Existing generation-based models generate generic and safe responses such as "So am I" or "I don't know" |
| Approach: | They propose to predict the mediators to preserve relevant information and auto-regressively incorporate the mediator into generating process. |
| Outcome: | The proposed model generates relevant and informative responses and outperforms the state-of-the-art in terms of automatic metrics and human evaluations. |
Reciprocal Learning of Knowledge Retriever and Response Ranker for Knowledge-Grounded Conversations (2022.coling-1)
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| Challenge: | Recent work on grounding dialogue agents with knowledge documents has sparked increased attention . hand-labeling data to that end is time-consuming and many datasets lack knowledge annotations . |
| Approach: | They propose a reciprocal learning approach to optimize a knowledge retriever and a response ranker for knowledge-grounded response retrieval without ground-truth knowledge labels. |
| Outcome: | The proposed model outperforms previous state-of-the-art methods on two public benchmarks. |
CR-GIS: Improving Conversational Recommendation via Goal-aware Interest Sequence Modeling (2022.coling-1)
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| Challenge: | Existing methods to determine a goal item by sequentially tracking users’ interests ignore the rich goal-aware implicit interest sequence patterns in a dialog. |
| Approach: | They propose to model goal-aware implicit user interest sequence patterns in a dialog and a hierarchical Star Transformer to guide multi-turn utterances generation. |
| Outcome: | The proposed framework achieves more accurate recommendations with more fluent and coherent dialog utterances. |
GRASP: Guiding Model with RelAtional Semantics Using Prompt for Dialogue Relation Extraction (2022.coling-1)
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| Challenge: | Existing studies utilize pre-trained language models with extensive features to supplement the low information density of the dialogue by multiple speakers. |
| Approach: | They propose a dialogue-based relation extraction task that leverages pre-trained language models to capture relational semantic clues of a given dialogue using an argument-aware prompt marker strategy and a relational clue detection task. |
| Outcome: | The proposed model achieves state-of-the-art on a DialogRE dataset even though it only leverages pre-trained language models without adding any extra layers. |
PEPDS: A Polite and Empathetic Persuasive Dialogue System for Charity Donation (2022.coling-1)
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| Challenge: | Empathy plays a crucial role in mediating the persuasive effects as it evokes cognitive and emotional processing conducive to persuasion. |
| Approach: | They propose to use a maximum likelihood estimate loss based model to design an efficient reward function consisting of five sub rewards viz. persuasion, emotion, Politeness-Strategy Consistency, Dialogue-Coherence and Non-repetitiveness. |
| Outcome: | The proposed system increases the rate of persuasive responses with emotion and politeness acknowledgement compared to the current state-of-the-art dialogue models while maintaining the linguistic quality. |
DialAug: Mixing up Dialogue Contexts in Contrastive Learning for Robust Conversational Modeling (2022.coling-1)
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| Challenge: | a conversational system can learn to rank response candidates for a given dialogue context by computing similarity between their vector representations. |
| Approach: | They propose a framework that incorporates augmented dialogue contexts into the learning objective. |
| Outcome: | The proposed framework outperforms existing methods and is more robust to perturbations seen during inference. |
A Closer Look at Few-Shot Out-of-Distribution Intent Detection (2022.coling-1)
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| Challenge: | Existing methods for few-shot out-of-distribution (OOD) intent detection are not adequate . despite its importance, few- shot OOD intent detection is a challenging problem . |
| Approach: | They propose a latent representation generation and self-supervision approach to solve few-shot OOD intent detection problem. |
| Outcome: | The proposed approach is highly effective and could improve state-of-the-art methods for few-shot OOD intent detection. |
CGIM: A Cycle Guided Interactive Learning Model for Consistency Identification in Task-oriented Dialogue (2022.coling-1)
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| Challenge: | Consistency identification in task-oriented dialog usually consists of three subtasks . a proposed model for consistency identification in dialog is based on an explicit interaction paradigm . |
| Approach: | They propose a cycle guided interactive learning model that makes information exchange explicit from all the three tasks. |
| Outcome: | The proposed model achieves state-of-the-art performance pushing the overall score to 56.3% (5.0% point absolute improvement) |
CorefDiffs: Co-referential and Differential Knowledge Flow in Document Grounded Conversations (2022.coling-1)
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| Challenge: | Document-grounded dialogs need smooth transitions between knowledge selected for generating responses. |
| Approach: | They propose a multi-document co-referential graph to capture inter- and intra-document relationships . they propose 'Coref-MDG' method to linearize static Coref-mDG into conversational sequence logic. |
| Outcome: | The proposed method outperforms the state-of-the-art by 9.5%, 7.4% and 8.2% on three public benchmarks. |
SelF-Eval: Self-supervised Fine-grained Dialogue Evaluation (2022.coling-1)
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| Challenge: | Existing evaluation metrics are expensive and easy to conduct but ineffective to reflect dialogue quality. |
| Approach: | They propose a self-supervised fine-grained dialogue evaluation framework which can automatically assign fine-granular scores for arbitrarily dialogue data. |
| Outcome: | The proposed framework is highly consistent with human evaluations and better than the state-of-the-art models. |
Open-Domain Dialog Evaluation Using Follow-Ups Likelihood (2022.coling-1)
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| Challenge: | Existing methods do not correlate strongly with human annotations. |
| Approach: | They propose a method that measures the probability that a language model will continue the conversation with a fixed set of follow-ups. |
| Outcome: | The proposed method achieves the highest correlation with human evaluations when compared against twelve existing methods. |
Joint Goal Segmentation and Goal Success Prediction on Multi-Domain Conversations (2022.coling-1)
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| Challenge: | Existing metrics to measure the performance of conversational AI assistants are difficult to establish due to their slow nature. |
| Approach: | They propose an automatic dialogue evaluation framework that performs goal segmentation and success prediction by adding multi-task learning heads. |
| Outcome: | The proposed model achieves on-par with human annotation compared to a gold annotation benchmark. |
Slot Dependency Modeling for Zero-Shot Cross-Domain Dialogue State Tracking (2022.coling-1)
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| Challenge: | Existing zero-shot learning methods ignore slot dependencies in a multidomain dialogue . experimental results show the effectiveness of our proposed method over existing state-of-art generation methods . |
| Approach: | They propose to use slot prompts combination, slot values demonstration and slot constraint object to model slot-slot dependency, slot-value dependency and slot-context dependency respectively. |
| Outcome: | The proposed method outperforms state-of-the-art methods under zero-shot/few-shot settings. |
Section-Aware Commonsense Knowledge-Grounded Dialogue Generation with Pre-trained Language Model (2022.coling-1)
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| Challenge: | Pre-trained language models can be expected to deepen the fusing of dialogue context and knowledge because of their superior ability of semantic understanding. |
| Approach: | They propose a two-stage framework to integrate a linearized knowledge into plan text using a ranking network PriorRanking to estimate the relevance of a retrieved knowledge fact. |
| Outcome: | The proposed framework improves the performance of pre-trained language models by using section-aware strategies to encode the linearized knowledge. |
Using Multi-Encoder Fusion Strategies to Improve Personalized Response Selection (2022.coling-1)
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| Challenge: | Existing systems that focus on persona do not explore well the correlation between persona and empathy. |
| Approach: | They propose a suite of fusion strategies that capture interaction between persona, emotion, and entailment information of the utterances. |
| Outcome: | The proposed model outperforms the previous methods by 2.3% on original personas and 1.9% on revised persona models in terms of hits@1 accuracy. |
A Multi-Dimensional, Cross-Domain and Hierarchy-Aware Neural Architecture for ISO-Standard Dialogue Act Tagging (2022.coling-1)
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| Challenge: | Dialogue Act tagging with ISO 24617-2 standard is a difficult task that requires multiple labels covering semantic, syntactic and pragmatic aspects of dialogue. |
| Approach: | They propose a neural architecture to increase Dialogue Act tagging accuracy by using low-frequency fine-grained tags. |
| Outcome: | The proposed model achieves state-of-the-art tagging results on DialogBank data set . it uses syntactic information in the form of Part-Of-Speech and dependency tags . |
SPACE-2: Tree-Structured Semi-Supervised Contrastive Pre-training for Task-Oriented Dialog Understanding (2022.coling-1)
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| Challenge: | Existing methods for dialog understanding only consider self-augmented dialogs as positive samples and treat all other dialogs like negative ones. |
| Approach: | They propose a tree-structured pre-trained conversation model which learns dialog representations from limited labeled dialogs and large-scale unlabeled dialog corpora via semi-supervised contrastive pre-training. |
| Outcome: | The proposed model can achieve state-of-the-art results on the DialoGLUE benchmark. |
ET5: A Novel End-to-end Framework for Conversational Machine Reading Comprehension (2022.coling-1)
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| Challenge: | Existing methods require three steps to understand text, but span extraction and question rephrasing steps are not fully exploited. |
| Approach: | They propose a framework for conversational machine reading comprehension based on shared parameter mechanism . experimental results show the proposed framework achieves new state-of-the-art results on the ShARC leaderboard . |
| Outcome: | The proposed framework achieves state-of-the-art on the ShARC leaderboard with the BLEU-4 score of 55.2. |
CoHS-CQG: Context and History Selection for Conversational Question Generation (2022.coling-1)
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| Challenge: | Existing studies focus on single-turn question generation, but few studies have studied the challenges of multiturn QG. |
| Approach: | They propose a two-stage conversational question generation framework that shortens the context and history of the input and calculates relevance scores. |
| Outcome: | The proposed framework achieves state-of-the-art on CoQA in answer-aware and answer-unaware settings. |
Semantic-based Pre-training for Dialogue Understanding (2022.coling-1)
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| Challenge: | Pre-trained language models are weak in understanding the main semantic meaning of a dialogue context. |
| Approach: | They propose a semantic-based framework that leverages explicit semantic knowledge to capture the core semantic information in dialogues during pre-training. |
| Outcome: | The proposed model is superior to existing models on chit-chats and task-oriented dialogues. |
Distribution Calibration for Out-of-Domain Detection with Bayesian Approximation (2022.coling-1)
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| Challenge: | Existing methods for OOD detection are overconfident for OD samples . lack of labeled OOD examples leads to poor prior knowledge about these unknown intents, making it challenging to detect OOD samples. |
| Approach: | They propose a Bayesian OOD detection framework to calibrate distribution uncertainty using Monte-Carlo Dropout. |
| Outcome: | The proposed framework gains 33.33% OOD F1 improvements with increasing only 0.41% inference time compared to previous methods. |
Tracking Satisfaction States for Customer Satisfaction Prediction in E-commerce Service Chatbots (2022.coling-1)
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| Challenge: | Existing models for customer satisfaction prediction (CSP) focus on analyzing subjective customer satisfaction in conversational service, but they are hard to represent the important dynamic satisfaction states throughout the customer journey. |
| Approach: | They propose a model to track customer satisfaction in chatbots using a dialogue-level classification module to represent the dynamic satisfaction states at each turn. |
| Outcome: | The proposed model outperforms baselines and shows that it significantly outperformed multiple baselines. |
Towards Multi-label Unknown Intent Detection (2022.coling-1)
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| Challenge: | Existing methods for multi-class unknown intent detection assume that each utterance has only one intent, which is not true because utterrances often contain multiple intents. |
| Approach: | They propose a task to detect whether an utterance contains the unknown intent by recognizing whether all intents contained in the utterant are known. |
| Outcome: | The proposed method significantly reduces the FPR95 on the MultiWOZ 2.3 dataset by 12.25% compared to the best baseline. |
Pan More Gold from the Sand: Refining Open-domain Dialogue Training with Noisy Self-Retrieval Generation (2022.coling-1)
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| Challenge: | Existing methods for generating open-domain dialogue systems underutilize training data. |
| Approach: | They propose a retrieval-generation training framework that takes advantage of heterogeneous training data by considering them as "evidence" they use BERTScore retrieval framework which gives better qualities of the training data, they show . |
| Outcome: | The proposed method performs well on zero-shot experiments and is more robust to real-world data. |
MulZDG: Multilingual Code-Switching Framework for Zero-shot Dialogue Generation (2022.coling-1)
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| Challenge: | Existing zero-shot dialogue generation systems rely on large-scale pre-trained language models. |
| Approach: | They propose a multilingual learning framework for zero-shot dialogue generation that can transfer knowledge from an English corpus to a non-English corpus with zero samples. |
| Outcome: | The proposed framework can transfer knowledge from an English corpus to a non-English corpus with zero samples. |
Target-Guided Open-Domain Conversation Planning (2022.coling-1)
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| Challenge: | Existing studies on goal-oriented conversational tasks lack planning . prior studies on this topic have focused on generating proactive behavior in agents . |
| Approach: | They propose a task to evaluate whether neural conversational agents have goal-oriented conversation planning abilities. |
| Outcome: | The proposed task evaluates whether neural conversational agents have goal-oriented conversation planning abilities. |
Does GPT-3 Generate Empathetic Dialogues? A Novel In-Context Example Selection Method and Automatic Evaluation Metric for Empathetic Dialogue Generation (2022.coling-1)
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| Challenge: | Empathy is a multi-dimensional concept consisting of cognitive and affective aspects. |
| Approach: | They propose two new in-context example selection methods that utilize emotion and situational information. |
| Outcome: | The proposed method is effective in measuring the degree of human empathy. |
DialogueEIN: Emotion Interaction Network for Dialogue Affective Analysis (2022.coling-1)
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| Challenge: | Emotion Recognition in Conversation (ERC) has attracted increasing research attention in recent years. |
| Approach: | They propose to model the emotional interactions between speakers to simulate the emotional inertia, emotional stimulus, global and local emotional evolution in dialogues. |
| Outcome: | The proposed model can achieve superior performance compared to state-of-the-art methods on four ERC benchmark datasets, IEMOCAP, MELD, EmoryNLP and DailyDialog. |
Towards Enhancing Health Coaching Dialogue in Low-Resource Settings (2022.coling-1)
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Yue Zhou, Barbara Di Eugenio, Brian Ziebart, Lisa Sharp, Bing Liu, Ben Gerber, Nikolaos Agadakos, Shweta Yadav
| Challenge: | Health coaching is cost-prohibitive due to its highly personalized nature. |
| Approach: | They propose to build a health coaching dialogue system that converses with patients . they propose to use simplified NLU and NLG frameworks and mechanism-conditioned empathetic response generation. |
| Outcome: | The proposed system generates more empathetic, fluent, and coherent responses . it outperforms the state-of-the-art in NLU tasks while requiring less annotations. |
Generalized Intent Discovery: Learning from Open World Dialogue System (2022.coling-1)
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| Challenge: | Existing intent classification models rely on a pre-defined intent set and supervised labels, which is limited in some practical scenarios. |
| Approach: | They propose to extend an IND intent classifier to an open-world intent set including IND and OOD intents. |
| Outcome: | The proposed task can classify IND and OOD intents while discovering new unlabeled OOD types incrementally. |
DialMed: A Dataset for Dialogue-based Medication Recommendation (2022.coling-1)
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| Challenge: | Existing studies on medication recommendation mainly rely on EHRs, but some details of interactions between doctors and patients may be ignored or omitted in EHR. |
| Approach: | They propose to use medical dialogues to recommend medications with medical dialogue data . they propose to model dialogue structure and disease knowledge aware network . |
| Outcome: | The proposed method is a promising solution to recommend medications with medical dialogues. |
Speaker Clustering in Textual Dialogue with Pairwise Utterance Relation and Cross-corpus Dialogue Act Supervision (2022.coling-1)
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| Challenge: | Existing models for textual dialogues do not include speaker annotations. |
| Approach: | They propose a speaker clustering model for textual dialogues that groups utterances without annotations so that the actual speakers are identical inside each cluster. |
| Outcome: | The proposed model outperforms the sequence classification baseline and benefits from the auxiliary dialogue act classification task. |
TopKG: Target-oriented Dialog via Global Planning on Knowledge Graph (2022.coling-1)
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| Challenge: | Existing target-oriented dialogs take a local and greedy strategy for response generation, where global planning is absent. |
| Approach: | They propose a global planning method for target-oriented dialog on a commonsense knowledge graph to adjust local response generation towards the global target. |
| Outcome: | The proposed method can reach the target with a higher success rate, fewer turns, and more coherent responses. |
Extractive Summarisation for German-language Data: A Text-level Approach with Discourse Features (2022.coling-1)
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| Challenge: | Using RST, extractive summarisation involves using select phrases and sentences as a summary, which still remains a strong method for producing summaries despite its simple nature. |
| Approach: | They propose to use RST-based features to analyse the connection between summary sentences and several RST features and transfer these insights to various automated summarisation models. |
| Outcome: | The proposed models are based on the best features proposed over the last 20+ years and incorporate the best ones into the proposed models. |
End-to-End Neural Bridging Resolution (2022.coling-1)
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| Challenge: | state-of-the-art resolvers for bridging resolution are weaker than entity coreference resolution. |
| Approach: | They evaluate bridging resolvers in an end-to-end setting and strengthen them with better encoders . they also try to gain a better understanding of them through perturbation experiments . |
| Outcome: | bridging resolvers are evaluated in an end-to-end setting and strengthened with better encoders . bribridging resolution is the task of identifying briating anaphors and linking them to their antecedents - a paper by the journal bribing resolution argues . |
Investigating the Performance of Transformer-Based NLI Models on Presuppositional Inferences (2022.coling-1)
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| Challenge: | Presuppositions are assumptions that are taken for granted by an utterance. |
| Approach: | They propose to use heuristics to create alternative "contrastive" test cases . they also analyze samples from ImpPres datasets to better understand their predictions . |
| Outcome: | The proposed model performs better on the ImpPres dataset than on the other datasets. |
Re-Examining FactBank: Predicting the Author’s Presentation of Factuality (2022.coling-1)
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| Challenge: | Previously published results on FactBank are no longer valid. |
| Approach: | They propose to correct a subset of FactBank data to improve performance . they use multiple training paradigms, data smoothing techniques, and polarity classifiers . |
| Outcome: | The proposed model improves performance on the FactBank dataset. |
The Role of Context and Uncertainty in Shallow Discourse Parsing (2022.coling-1)
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| Challenge: | Discourse parsing has proven to be useful for a number of NLP tasks that require complex reasoning. |
| Approach: | They hypothesize that context plays an important role in accurate human annotation and add uncertainty measures can improve model accuracy and calibration. |
| Outcome: | The proposed model can be better calibrated by adding uncertainty measures to models with better accuracy and calibration. |
Improving Commonsense Contingent Reasoning by Pseudo-data and Its Application to the Related Tasks (2022.coling-1)
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| Challenge: | Contingent reasoning is one of the essential abilities in natural language understanding . despite advances in deep learning, the task of contingent reasoning is still difficult for computers . |
| Approach: | They propose to generate large-scale pseudo-problems and incorporate them into training . they also investigate the generality of contingent knowledge through quantitative evaluation . |
| Outcome: | The proposed method is able to evaluate the generality of contingent knowledge through transfer learning. |
A Survey in Automatic Irony Processing: Linguistic, Cognitive, and Multi-X Perspectives (2022.coling-1)
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| Challenge: | figurative language research has focused on sarcasm and irony, but there is still a gap in the field. |
| Approach: | They propose to review computational irony, cognitive science, and neural models of irony processing . they aim to encourage a balanced and equal research environment in figurative languages . |
| Outcome: | The proposed multi-X irony processing perspectives will provide an overview of computational irony, insights from linguisic theory and cognitive science, and interactions with downstream NLP tasks. |
Towards Identifying Alternative-Lexicalization Signals of Discourse Relations (2022.coling-1)
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| Challenge: | Existing shallow discourse parsing methods have been limited to identifying relations signaled by a discourse connective and those without a signal. |
| Approach: | They propose to identify relations signalled by a discourse connective and those without . they compare a pattern-based approach and a sequence labeling model . |
| Outcome: | The proposed approach is based on a pattern-based approach and a sequence labeling model. |
Topicalization in Language Models: A Case Study on Japanese (2022.coling-1)
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| Challenge: | a recent study has shown that neural language models can capture discourse-level preferences in text generation . a particular aspect of discourse is the topic-comment structure . |
| Approach: | They analyze whether neural language models can capture discourse-level preferences in text generation . they use Japanese language and crowdsourced human topicalization judgment data . |
| Outcome: | The proposed model can capture human-like generalizations in discourse-level linguistic aspects. |
“No, They Did Not”: Dialogue Response Dynamics in Pre-trained Language Models (2022.coling-1)
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| Challenge: | a critical component of competence in language is being able to identify relevant components of an utterance . sensitivity to at-issueness and ellipsis is examined in pre-trained language models . authors find mixed results with respect to capturing full range of dynamics involved in targeting at- issue content . |
| Approach: | They examine sensitivity to at-issueness and ellipsis in pre-trained language models . they find that models show a preference for responses that target main clause content . |
| Outcome: | The proposed models show strong sensitivity to at-issueness and ellipsis dynamics . the results show that the models lack grasp of the dynamics involved in targeting at- issue versus not-at-issue content . |
New or Old? Exploring How Pre-Trained Language Models Represent Discourse Entities (2022.coling-1)
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| Challenge: | Recent research shows pre-trained language models learn to encode syntactic knowledge to a certain degree. |
| Approach: | They propose to investigate the information-status of entities as discourse-new or discourse-old . they use binary classification and sequence labeling to investigate their ability to encode syntactic knowledge . |
| Outcome: | The proposed models encode information on whether an entity has been introduced before or not in the discourse. |
Dialo-AP: A Dependency Parsing Based Argument Parser for Dialogues (2022.coling-1)
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| Challenge: | a recent work on argument mining has focused on parsing monologues, while neglecting dialogues. |
| Approach: | They propose an end-to-end argument parser that constructs argument graphs from dialogues . they use extensive pre-training and curriculum learning to train AM . |
| Outcome: | The proposed system performs all sub-tasks of AM and achieves significant improvements . it is compared to existing systems and validated through human evaluation . |
ConnPrompt: Connective-cloze Prompt Learning for Implicit Discourse Relation Recognition (2022.coling-1)
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| Challenge: | Existing paradigms for Implicit Discourse Relation Recognition (IDRR) do not exploit linguistic evidence embedded in the pre-training process. |
| Approach: | They propose a new paradigm to detect and classify relation sense between two text segments without an explicit connective. |
| Outcome: | The proposed method significantly outperforms the state-of-the-art algorithms even with fewer training data. |
A Distance-Aware Multi-Task Framework for Conversational Discourse Parsing (2022.coling-1)
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| Challenge: | Existing studies have focused on graph-based and transition-based discourse parsing, but no study has investigated the advantages of both paradigms for conversational discourse paring. |
| Approach: | They propose a distance-aware multi-task framework that incorporates the strengths of transition-based paradigms to facilitate conversational discourse parsing. |
| Outcome: | The proposed framework improves the graph-based paradigm on long-distance dependency links. |
Linguistically Motivated Features for Classifying Shorter Text into Fiction and Non-Fiction Genre (2022.coling-1)
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| Challenge: | linguistically motivated features are used to classify paragraph-level text into fiction and non-fiction genres. |
| Approach: | They deploy linguistically motivated features to classify paragraph-level text into fiction and non-fiction genres using a logistic regression model. |
| Outcome: | The proposed model gives 15.56% accuracy jump over baseline model . the proposed model also transfers over to another dataset, Baby BNC corpus . |
Semantic Sentence Matching via Interacting Syntax Graphs (2022.coling-1)
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| Challenge: | Extensive research efforts have been devoted to the task of matching two natural language sentences. |
| Approach: | They propose to embed syntactic structures into an embedding vector and combine them with other features to predict matching scores. |
| Outcome: | The proposed method outperforms the state-of-the-art methods on three public datasets and can interpret sentences in interpretable way. |
Hierarchical Information Matters: Text Classification via Tree Based Graph Neural Network (2022.coling-1)
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| Challenge: | Text classification is a primary task in natural language processing (NLP). |
| Approach: | They propose a graph neural network (HINT) that makes full use of hierarchical information contained in the text for the task of text classification. |
| Outcome: | The proposed method outperforms the state-of-the-art methods on popular benchmarks while having a simple structure and few parameters. |
SelfMix: Robust Learning against Textual Label Noise with Self-Mixup Training (2022.coling-1)
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| Challenge: | Existing methods to handle label noise in text classification tasks are limited to visual data. |
| Approach: | They propose a method to handle label noise in text classification tasks using a Gaussian Mixture Model. |
| Outcome: | The proposed method outperforms baselines on three types of text classification tasks on visual and textual data. |
Community Topic: Topic Model Inference by Consecutive Word Community Discovery (2022.coling-1)
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| Challenge: | a new topic modelling algorithm is developed to help researchers understand large corpora . community topic can be used to find coherent topics at various scales . |
| Approach: | They propose a topic-modeling algorithm that extracts communities from term co-occurrence networks and compares it with Latent Dirichlet Allocation and top2vec. |
| Outcome: | The proposed algorithm can find coherent topics at various scales. |
Where to Attack: A Dynamic Locator Model for Backdoor Attack in Text Classifications (2022.coling-1)
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| Challenge: | BackDoor Attack (BDA) study aims to train a poisoned model with clean data and some trigger-embedded instances to perform normally on normal inputs. |
| Approach: | They propose to train a poisoned model with clean and poisonest inputs . they propose to use triggers to predict those poisonets as target labels . |
| Outcome: | The proposed model can predict P2P dynamically without human intervention. |
Locally Distributed Activation Vectors for Guided Feature Attribution (2022.coling-1)
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| Challenge: | Existing methods to explain predictions of deep neural networks are unstable and do not always provide faithful explanations to the target model. |
| Approach: | They propose a method to learn explanations-specific representations while constructing deep network models for text classification. |
| Outcome: | The proposed method improves model interpretability while preserving predictive performance. |
Addressing Leakage in Self-Supervised Contextualized Code Retrieval (2022.coling-1)
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| Challenge: | a recent study addresses the use of contextualized code retrieval to fill gaps in a partial input program. |
| Approach: | They propose a self-supervised approach to contextualized code retrieval . they propose mutual identifier masking, dedentation, and the selection of syntax-aligned targets . |
| Outcome: | The proposed approach improves retrieval substantially and yields state-of-the-art results for code clone and defect detection. |
A Domain Knowledge Enhanced Pre-Trained Language Model for Vertical Search: Case Study on Medicinal Products (2022.coling-1)
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| Challenge: | Existing pre-trained language models lack medicinal product knowledge for product vertical search. |
| Approach: | They propose a biomedical knowledge enhanced pre-trained language model for medicinal product vertical search using ELECTRA’s replaced token detection (RTD) pre-training. |
| Outcome: | The proposed model improves query-title relevance, query intent classification, and named entity recognition in query. |
CONCRETE: Improving Cross-lingual Fact-checking with Cross-lingual Retrieval (2022.coling-1)
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| Challenge: | Existing fact-checking approaches focus on claims made in English due to data scarcity issue in other languages. |
| Approach: | They propose a fact-checking framework augmented with cross-lingual retrieval that aggregates evidence retrieved from multiple languages through a cross-linguistic retriever. |
| Outcome: | The proposed framework achieves 2.23% absolute F1 improvement over previous systems on a X-Fact dataset. |
E-VarM: Enhanced Variational Word Masks to Improve the Interpretability of Text Classification Models (2022.coling-1)
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Ling Ge, ChunMing Hu, Guanghui Ma, Junshuang Wu, Junfan Chen, JiHong Liu, Hong Zhang, Wenyi Qin, Richong Zhang
| Challenge: | Empirical studies show that our approach outperforms the SOTA methods in improving the interpretability of text classification models. |
| Approach: | They propose an enhanced variational word masks approach that exploits the Variational Information Bottleneck to obtain task-specific words. |
| Outcome: | Empirical results show that the proposed method outperforms the SOTA methods in improving the interpretability of the model. |
Attribute Injection for Pretrained Language Models: A New Benchmark and an Efficient Method (2022.coling-1)
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| Challenge: | Recent models rely on pretrained language models that use metadata as inputs . however, these methods are either nontrivial or cost-ineffective . |
| Approach: | They propose a benchmark for evaluating attribute injection models using eight datasets . they extend adapters to include attributes independently of or jointly with the text . |
| Outcome: | The proposed method outperforms previous methods and achieves state-of-the-art performance on all datasets. |
Towards Robust Neural Retrieval with Source Domain Synthetic Pre-Finetuning (2022.coling-1)
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Revanth Gangi Reddy, Vikas Yadav, Md Arafat Sultan, Martin Franz, Vittorio Castelli, Heng Ji, Avirup Sil
| Challenge: | Existing neural IR systems rely on lexical matching for query-passage alignment, while masked language models use a dual encoder architecture to encode passages and questions into continuous vector representations. |
| Approach: | They propose to enhance the out-of-domain generalization of Dense Passage Retrieval (DPR) through synthetic data augmentation only in the source domain. |
| Outcome: | The proposed model outperforms existing models in in-domain and zero-shot evaluations on Wikipedia-based datasets. |
Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval (2022.coling-1)
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| Challenge: | State-of-the-art neural rankers are notoriously data-hungry and rarely used in multilingual and cross-lingual retrieval settings. |
| Approach: | They propose to use Sparse Fine-Tuning Masks and Adapters to transfer rankers trained on English data to other languages and cross-lingual setups by means of multilingual encoders. |
| Outcome: | The proposed methods outperform standard zero-shot transfer with full MMT fine-tuning while being more modular and reducing training times. |
LIME: Weakly-Supervised Text Classification without Seeds (2022.coling-1)
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| Challenge: | Existing approaches to weakly-supervised text classification use only label names as sources of supervision. |
| Approach: | They propose a framework for weakly-supervised text classification that replaces seed-word generation with entailment-based pseudo-classification. |
| Outcome: | The proposed framework outperforms baselines and state-of-the-art in 4 benchmarks. |
Multi-Stage Framework with Refinement Based Point Set Registration for Unsupervised Bi-Lingual Word Alignment (2022.coling-1)
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| Challenge: | Existing unsupervised approaches to cross-lingual word embeddings suffer from instability and convergence issues. |
| Approach: | They propose a multi-stage framework for unsupervised mapping of bi-lingual word embeddings onto a shared vector space by combining adversarial initialization, refinement procedure and point set registration. |
| Outcome: | The proposed framework shows robustness against variable adversarial performance on diverse languages. |
EM-PERSONA: EMotion-assisted Deep Neural Framework for PERSONAlity Subtyping from Suicide Notes (2022.coling-1)
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| Challenge: | Suicide continues to be one of the significant causes of death worldwide . EMotion-assisted personality subtyping is a novel approach to identify personality traits from suicide notes . |
| Approach: | They propose to use a PERSONAlity Detection Framework to identify personality traits from suicide notes and annotate them using a benchmark dataset. |
| Outcome: | The proposed method outperforms baselines on comprehensive evaluation using multiple state-of-the-art systems. |
Dense Template Retrieval for Customer Support (2022.coling-1)
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| Challenge: | Templated answers are used to cover a wide range of topics, but the number of templates is often too high for an agent to manually search. |
| Approach: | They propose a dense retrieval framework that adapts a standard in-batch negatives technique to support unpaired sampling of queries and templates. |
| Outcome: | The proposed approach improves performance and training speed over more standard methods. |
Exploring Label Hierarchy in a Generative Way for Hierarchical Text Classification (2022.coling-1)
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| Challenge: | Existing methods for hierarchical text classification are lacking in the field of natural language processing. |
| Approach: | They propose a hierarchy-aware T5 model with path-adaptive attention mechanism to exploit hierarchical dependency across different levels. |
| Outcome: | The proposed model outperforms state-of-the-art models especially in Macro-F1 and low Macro. |
MuSeCLIR: A Multiple Senses and Cross-lingual Information Retrieval Dataset (2022.coling-1)
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| Challenge: | Existing datasets for cross-lingual information retrieval (CLIR) are dominated by searches for named entity mentions, which does not provide a good measure for disambiguation performance. |
| Approach: | They propose a dataset to evaluate CLIR systems' disambiguation ability based on polysemous common nouns with multiple possible translations. |
| Outcome: | The proposed dataset shows that it has a higher requirement on the ability of CLIR systems to disambiguate query terms. |
Complicate Then Simplify: A Novel Way to Explore Pre-trained Models for Text Classification (2022.coling-1)
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| Challenge: | Existing frameworks for text classification employing pre-trained models are constrained by the difficulty of the task. |
| Approach: | They propose a framework which implements a two-stage training strategy to fully exploit the knowledge in pre-trained models. |
| Outcome: | The proposed framework outperforms state-of-the-art classification models on six text classification corpora. |
Adaptive Feature Discrimination and Denoising for Asymmetric Text Matching (2022.coling-1)
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| Challenge: | Existing models focus on asymmetric text matching but rarely perform feature denoising . existing models focus only on recognizing discriminative features and filtering out irrelevant features . |
| Approach: | They propose a novel adaptive feature discrimination and denoising model for asymmetric text matching . it explicitly distinguishes discriminative features and filters out irrelevant features in context . |
| Outcome: | The proposed model achieves significant performance gains over current state-of-the-art models on four real-world datasets. |
Rethinking Data Augmentation in Text-to-text Paradigm (2022.coling-1)
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| Challenge: | Existing approaches to augment training data are limited or marginal, or even diminishing or adverse especially given original training corpus is relatively sufficient or the backbone classifiers are PLM based. |
| Approach: | They propose to integrate text-to-text language models and construct a new two-phase framework for augmentation using two novel schemes. |
| Outcome: | The proposed framework synthesizes new samples benefiting from the knowledge learned from pre-trained language models on two public classification datasets and shows remarkable gains. |
ConTextING: Granting Document-Wise Contextual Embeddings to Graph Neural Networks for Inductive Text Classification (2022.coling-1)
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| Challenge: | Graph neural networks (GNNs) are used to learn document representation from graph structures. |
| Approach: | They propose a unified model with a joint training mechanism to learn from document embeddings and contextual word interactions simultaneously. |
| Outcome: | The proposed model outperforms pure inductive GNNs and BERT-style models . the proposed model also has a joint training mechanism to learn from document embeddings and contextual word interactions simultaneously. |
Virtual Knowledge Graph Construction for Zero-Shot Domain-Specific Document Retrieval (2022.coling-1)
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| Challenge: | Domain-specific documents cover terminologies and specialized knowledge. |
| Approach: | They propose a domain-specific document retrieval method that embeds a document into a graph of entities and their relations into . they compare the unsupervised method with previous approaches and use it to compute relevance between queries and documents. |
| Outcome: | The proposed method outperforms baselines and fully-supervised bi-encoders in a zero-shot setting and outperformed bi-supervised approaches. |
MICO: Selective Search with Mutual Information Co-training (2022.coling-1)
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| Challenge: | Selective search is designed to reduce the latency and computation in modern large-scale search systems. |
| Approach: | They propose a mutual information CO-training framework for selective search with minimal supervision using the search logs. |
| Outcome: | The proposed framework outperforms existing competitive benchmarks on multiple metrics and significantly outperformed existing baselines. |
DPTDR: Deep Prompt Tuning for Dense Passage Retrieval (2022.coling-1)
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| Challenge: | Recent studies show that prompt tuning is unfriendly for industrial deployment in dense retrieval tasks. |
| Approach: | They propose to apply prompt tuning to dense retrieval tasks to reduce deployment cost . they propose to use retrieval-oriented intermediate pretraining and unified negative mining . |
| Outcome: | The proposed method outperforms state-of-the-art models on MS-MARCO and Natural Questions. |
BERT-Flow-VAE: A Weakly-supervised Model for Multi-Label Text Classification (2022.coling-1)
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| Challenge: | Multi-label Text Classification (MLTC) is a task of categorizing documents into one or more topics. Fully-supervised learning methods are undesirable for this task because of the diversity of domains of application and cost of manual labelling. |
| Approach: | They propose a Weakly-Supervised Multi-Label Text Classification model that produces BERT sentence embeddings and calibrates them using a flow model. |
| Outcome: | The proposed model outperforms baseline models in key metrics and achieves 84% performance on multi-label datasets. |
Welcome to the Modern World of Pronouns: Identity-Inclusive Natural Language Processing beyond Gender (2022.coling-1)
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| Challenge: | Current modeling of 3rd person pronouns ignores neopronoun phenomena like naive pronounes, which are not (yet) widely established. |
| Approach: | They propose to validate existing and novel approaches for modeling 3rd person pronouns in language technology and validate them through a survey. |
| Outcome: | The proposed model excludes non-binary users, while ignoring gender-specific phenomena. |
Threat Scenarios and Best Practices to Detect Neural Fake News (2022.coling-1)
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| Challenge: | During the COVID-19 pandemic, inaccurate information made it hard for people to find reliable guidance when they needed it. |
| Approach: | They propose to use pretrained language models to generate fluent, original text . they argue that strong detectors should be released along with new generators . |
| Outcome: | The proposed system is prone to shortcut learning and should be released along with new generators. |
From Polarity to Intensity: Mining Morality from Semantic Space (2022.coling-1)
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| Challenge: | Existing approaches to compute moral intensity are limited to word-level measurement and heavily rely on human labelling. |
| Approach: | They propose a weakly-supervised framework that can automatically measure moral intensity from text. |
| Outcome: | The proposed framework can measure moral intensity from text with moral polarity labels, which are more robust and easier to acquire. |
SOS: Systematic Offensive Stereotyping Bias in Word Embeddings (2022.coling-1)
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| Challenge: | Systematic Offensive Stereotyping (SOS) in word embeddings could lead to associating marginalised groups with hate speech and profanity. |
| Approach: | They propose a quantitative measure of the systematic offensive stereotyping (SOS) in word embeddings and validate it in most commonly used word embeds. |
| Outcome: | The proposed measure correlates with published statistics on online extremism, but does not explain hate speech detection models. |
Bigger Data or Fairer Data? Augmenting BERT via Active Sampling for Educational Text Classification (2022.coling-1)
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| Challenge: | Pretrained Language Models (PLMs) encode bias against protected groups in the representations they learn, which may harm the prediction fairness of downstream models. |
| Approach: | They propose to quantify the awareness that a pretrained language model (BERT) has regarding people’s protected attributes and augment it to enhance prediction fairness of downstream models. |
| Outcome: | The proposed method improves fairness and accuracy of models by inhibiting the awareness of protected attributes in the PLMs. |
Debiasing Word Embeddings with Nonlinear Geometry (2022.coling-1)
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| Challenge: | Existing methods for debiasing word embeddings are limited to individual social categories . however, real-world corpora typically present multiple social categories that may correlate or intersect with each other. |
| Approach: | They propose a method to debias word embeddings using nonlinear geometry of individual biases. |
| Outcome: | Empirical results show that the proposed method mitigates biases associated with individual social categories and treats each category in isolation. |
Debiasing Isn’t Enough! – on the Effectiveness of Debiasing MLMs and Their Social Biases in Downstream Tasks (2022.coling-1)
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| Challenge: | Existing measures for social bias evaluation are inadequate for MLMs to accurately evaluate the social biases in their systems. |
| Approach: | They propose task-agnostic intrinsic and task-specific extrinsic social bias evaluation measures for MLMs that use different methods to re-learn social biases during fine-tuning on downstream tasks. |
| Outcome: | The findings highlight the limitations of existing MLM bias evaluation measures and raise concerns on the deployment of MLMs in downstream applications using those measures. |
Quantifying Bias from Decoding Techniques in Natural Language Generation (2022.coling-1)
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| Challenge: | Natural language generation (NLG) models can propagate social bias towards particular demography. |
| Approach: | They propose to examine whether bias metrics like toxicity and sentiment are impacted by decoding techniques that use stochastic decoding. |
| Outcome: | The proposed methods reveal the imperative of testing inference time bias and provide evidence on the usefulness of inspecting the entire decoding spectrum. |
A Study of Implicit Bias in Pretrained Language Models against People with Disabilities (2022.coling-1)
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| Challenge: | Pretrained language models exhibit sociodemographic biases, such as against gender and race, raising concerns of downstream biase in language technologies. |
| Approach: | They propose to use word embedding-based and transformer-based PLMs to test for the presence of biases against people with disabilities (PWDs) |
| Outcome: | The proposed models favor ableist language, despite their sociodemographic biases against race and gender. |
Social Norms-Grounded Machine Ethics in Complex Narrative Situation (2022.coling-1)
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| Challenge: | Recent studies focus on data-driven methods to judge the ethics of complex real-world narratives but face two major challenges: they cannot handle dilemma situations due to a lack of basic knowledge about social norms; and they focus on sparse situation-level judgment regardless of the social norm. |
| Approach: | They propose to complement a complex situation with grounded social norms by a norm-supported ethical judgment model in line with neural module networks to alleviate dilemma situations and improve norm-level explainability. |
| Outcome: | The proposed model improves state-of-the-art performance on two narrative judgment benchmarks. |
Bias at a Second Glance: A Deep Dive into Bias for German Educational Peer-Review Data Modeling (2022.coling-1)
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| Challenge: | Existing studies have highlighted a variety of biases in pre-trained language models . however, these studies focus on fine-grained analysis of educational corpora and text that is not English . |
| Approach: | They analyze bias across text and through multiple architectures on a corpus of 9,165 German peer-reviews collected from university students over five years. |
| Outcome: | The proposed dataset shows that pre-trained language models exhibit conceptual, racial, and gender biases. |
Dynamic Relevance Graph Network for Knowledge-Aware Question Answering (2022.coling-1)
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| Challenge: | Existing approaches to solve commonsense question answering problems often miss some edges between entities, which breaks the reasoning chain. |
| Approach: | They propose a graph neural network architecture that uses relevance as graph edges to establish new edges dynamically for learning node representations in the graph network. |
| Outcome: | The proposed approach shows competitive performance on two QA benchmarks, CommonsenseQA and OpenbookQA, compared to the state-of-the-art published results. |
SISER: Semantic-Infused Selective Graph Reasoning for Fact Verification (2022.coling-1)
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| Challenge: | Existing graph-based methods for fact verification use semantic graphs, which are based on evidence sentences. |
| Approach: | They propose to use semantic-level graph reasoning to inject its reasoning-enhanced representation into other graph-based and sequence-based reasoning methods. |
| Outcome: | The proposed method outperforms the previous graph-based methods and achieves state-of-the-art performance on a large-scale dataset for Fact Extraction and VERification (FEVER). |
Answering Numerical Reasoning Questions in Table-Text Hybrid Contents with Graph-based Encoder and Tree-based Decoder (2022.coling-1)
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| Challenge: | Existing methods for numerical reasoning are not flexible enough to handle diverse expressions. |
| Approach: | They propose a Relational Graph enhanced Hybrid table-text Numerical reasoning model with Tree decoder which captures relationship between numerical value, table schema, and text information on the encoder side. |
| Outcome: | The proposed model outperforms the baseline model and achieves state-of-the-art results on the publicly available tabletext hybrid QA benchmark. |
Perform like an Engine: A Closed-Loop Neural-Symbolic Learning Framework for Knowledge Graph Inference (2022.coling-1)
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| Challenge: | Existing knowledge graphs are incomplete and therefore lack interpretability. |
| Approach: | They propose a closed-loop neural-symbolic learning framework EngineKG to address the natural incompleteness of knowledge graphs. |
| Outcome: | The proposed model outperforms baselines on link prediction tasks on four real-world datasets. |
Table-based Fact Verification with Self-labeled Keypoint Alignment (2022.coling-1)
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| Challenge: | Existing methods for fact verification rely on graph feature or data augmentation but fail to investigate evidence correlation between statement and table effectively. |
| Approach: | They propose a self-labeled keypoint alignment model to explore correlation between statement and table . they propose integrating a mixture-of experts block to integrate interacted information . |
| Outcome: | The proposed model outperforms the state-of-the-art models and captures interpretable evidence words on three widely-studied datasets. |
IMCI: Integrate Multi-view Contextual Information for Fact Extraction and Verification (2022.coling-1)
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| Challenge: | Existing models for fact extraction and verification fail to utilize multi-view contextual information. |
| Approach: | They propose to integrate multi-view contextual information (IMCI) for fact extraction and verification by combining contextual information with inter-document context. |
| Outcome: | The proposed framework achieves state-of-the-art performance on the open-domain Wikipedia task with a winning FEVER score of 73.96% and label accuracy of 77.25% on the online blind test set. |
Prompt Combines Paraphrase: Teaching Pre-trained Models to Understand Rare Biomedical Words (2022.coling-1)
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Haochun Wang, Chi Liu, Nuwa Xi, Sendong Zhao, Meizhi Ju, Shiwei Zhang, Ziheng Zhang, Yefeng Zheng, Bing Qin, Ting Liu
| Challenge: | Pre-trained models perform poorly with limited data and rare biomedical words. |
| Approach: | They propose to use prompt to fine-tune pre-trained models for biomedical domain tuning with a simple approach. |
| Outcome: | The proposed method achieves up to 6% improvement in biomedical natural language inference task without any extra parameters or training steps using few-shot vanilla prompt settings. |
Self-Supervised Intermediate Fine-Tuning of Biomedical Language Models for Interpreting Patient Case Descriptions (2022.coling-1)
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| Challenge: | Existing work has found that biomedical language models lack the knowledge needed for such tasks. |
| Approach: | They propose to fine-tune biomedical language models on the task of predicting masked medical concepts from PubMed abstracts to improve their performance. |
| Outcome: | The proposed strategy improves the performance of biomedical language models on the task of predicting masked medical concepts from patient case descriptions. |
Evaluating and Mitigating Inherent Linguistic Bias of African American English through Inference (2022.coling-1)
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| Challenge: | Recent studies show that NLP models trained on standard English produce biased outcomes against underrepresented English varieties. |
| Approach: | They propose a morphosyntactically-informed rule-based translation method that uses a greedy algorithm to debiase NLP models. |
| Outcome: | The proposed framework outperforms large language models while maintaining or improving the prediction performance. |
Can We Guide a Multi-Hop Reasoning Language Model to Incrementally Learn at Each Single-Hop? (2022.coling-1)
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| Challenge: | Recent developments have shown that pre-trained language models are effective soft reasoners over language. |
| Approach: | They propose to model multi-hop reasoning process as a sequence of explicit single-hop steps. |
| Outcome: | The proposed model improves on multiple-choice question answering and reading comprehension with 68.4% and 16.0% w.r.t. classic PLMs. |
Modeling Hierarchical Reasoning Chains by Linking Discourse Units and Key Phrases for Reading Comprehension (2022.coling-1)
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| Challenge: | Existing methods of logical reasoning focus on entity-aware information but ignore hierarchical relations that may even have mutual effects. |
| Approach: | They propose a holistic graph network that deals with context at both discourse-level and word-level as the basis for logical reasoning. |
| Outcome: | The proposed method improves on logical reasoning QA datasets and natural language inference datasets. |
Hierarchical Representation-based Dynamic Reasoning Network for Biomedical Question Answering (2022.coling-1)
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Jianguo Mao, Jiyuan Zhang, Zengfeng Zeng, Weihua Peng, Wenbin Jiang, Xiangdong Wang, Hong Liu, Yajuan Lyu
| Challenge: | Existing models of biomedical question answering are limited in their ability to predict answers . a new model improves the performance of existing models, but the code will be released after the paper is published. |
| Approach: | They propose a hierarchical representation-based dynamic reasoning network to solve biomedical problems. |
| Outcome: | The proposed model significantly improves on three mainstream biomedical datasets . the code will be released after the paper is published . |
ArT: All-round Thinker for Unsupervised Commonsense Question Answering (2022.coling-1)
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| Challenge: | Existing work on commonsense QA requires labeled training data for its success . existing work relies on large-scale in-domain or out-of-domain labeles or fails to generate knowledge of high quality in a general way. |
| Approach: | They propose an approach to commonsense question-answering (QA) that takes association during knowledge generation. |
| Outcome: | The proposed model outperforms existing models on commonsense QA benchmarks. |
Teaching Neural Module Networks to Do Arithmetic (2022.coling-1)
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| Challenge: | Neural Module Networks (NMNs) have limited reasoning abilities and lack numerical reasoning capability. |
| Approach: | They propose to integrate the original question in the interpreter and introduce addition and subtraction modules that perform numerical reasoning over numbers. |
| Outcome: | The proposed methods outperform previous state-of-the-art models on a subset of DROP and achieve competitive reasoning performance. |
An Augmented Benchmark Dataset for Geometric Question Answering through Dual Parallel Text Encoding (2022.coling-1)
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| Challenge: | Existing methods for solving geometric problems are limited due to lack of high-quality datasets and efficient neural solvers. |
| Approach: | They propose to annotate 2,518 geometric problems with richer types and greater difficulty using a benchmark dataset. |
| Outcome: | The proposed method improves the accuracy of automatic geometric problem solving to 66.09%. |
Competence-based Question Generation (2022.coling-1)
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| Challenge: | Existing models of natural language understanding rely on question answering and logical inference benchmark challenges to evaluate performance of systems. |
| Approach: | They propose a method to generate CB questions using English cooking recipes . they argue that a broader effort needs to be put on measuring linguistic competencies . |
| Outcome: | The proposed method performs poorly on large pretrained language models until they are provided with additional contextualized semantic information. |
Coalescing Global and Local Information for Procedural Text Understanding (2022.coling-1)
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| Challenge: | Existing models for procedural text understanding have low precision or low recall . et al., 2012, pp. 106-106. |
| Approach: | They propose a model that builds entity- and timestep-aware input representations . they extend the model with additional output layers and integrate it into a story reasoning framework . |
| Outcome: | The proposed model achieves state-of-the-art on a popular procedural text understanding dataset and on 'story reasoning benchmark' it integrates the model with additional output layers and improves on the previous models. |
Original Content Is All You Need! an Empirical Study on Leveraging Answer Summary for WikiHowQA Answer Selection Task (2022.coling-1)
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Liang Wen, Juan Li, Houfeng Wang, Yingwei Luo, Xiaolin Wang, Xiaodong Zhang, Zhicong Cheng, Dawei Yin
| Challenge: | Existing answer selection approaches for community question answering lack additional answer summaries due to redundancy and lengthiness issues of crowdsourced answers. |
| Approach: | They constructed a dataset which contains a corresponding reference summary for each original lengthy answer. |
| Outcome: | The proposed model improves the performance of a question and candidate answer on a WikiHowQA dataset. |
Case-Based Abductive Natural Language Inference (2022.coling-1)
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| Challenge: | Recent approaches for multi-hop inference construct explanations considering each test case in isolation, but semantic drift causes wrong conclusions. |
| Approach: | They propose an abductive framework for multi-hop NLI exploring the retrieve-reuse-refine paradigm in Case-Based Reasoning. |
| Outcome: | The proposed model can be integrated with sparse and dense pre-trained encoders to improve multi-hop inference, or adopted as an evidence retriever for Transformers. |
Semantic Structure Based Query Graph Prediction for Question Answering over Knowledge Graph (2022.coling-1)
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| Challenge: | Existing approaches for query graph generation ignore the semantic structure of a question . Existing methods ignore the structure of the question, resulting in noisy query graph candidates. |
| Approach: | They propose to build query graphs from natural language questions to predict semantic structure of a question. |
| Outcome: | The proposed method can predict the semantic structure of a question using six semantic structures from common questions in KGQA. |
Repo4QA: Answering Coding Questions via Dense Retrieval on GitHub Repositories (2022.coling-1)
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| Challenge: | Stack Overflow and GitHub are open source communities that are gaining popularity . developers need to raise programming questions in coding forums and navigate to GitHub repositories . |
| Approach: | They propose a questionrepository matching task that bridges the gap between repositories and real-world coding questions. |
| Outcome: | The proposed model outperforms state-of-the-art methods on coding questions and repositories . it can find suitable coding repositoriels and bridge the gap between them . |
Addressing Limitations of Encoder-Decoder Based Approach to Text-to-SQL (2022.coling-1)
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| Challenge: | Existing attempts on Text-to-SQL task show a dramatic decline in performance for new databases. |
| Approach: | They propose a hybrid system that integrates rule-based and deep learning components to improve model accuracy. |
| Outcome: | The proposed system achieves double-digit percentage improvement for non-Spider databases. |
Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering (2022.coling-1)
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| Challenge: | Existing question answering models can achieve high performance on simple questions that require a single fact lookup. |
| Approach: | They introduce a multilingual question-answering dataset called Mintaka . it includes 8 types of complex questions, including superlative, intersection, and multi-hop questions . they run baselines over Mintak, which achieves 38% hits@1 in English . |
| Outcome: | The proposed model achieves 38% hits@1 in English and 31% hits@1, multilingually. |
Can Edge Probing Tests Reveal Linguistic Knowledge in QA Models? (2022.coling-1)
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| Challenge: | grammatical knowledge is encoded in large pre-trained language models (LMs) this is done through supervised classification tasks to predict the grammamatical properties of a span using only the token representations coming from the LM encoder. |
| Approach: | They propose to use a supervised 'edge probing' task to detect grammatical knowledge in large pre-trained language models (LMs) this is done by encoding grammamatical properties using only token representations coming from the LM encoder. |
| Outcome: | The proposed model performs well when fine-tuned or in adversarial situations where the model is forced to learn wrong correlations. |
Conversational QA Dataset Generation with Answer Revision (2022.coling-1)
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| Challenge: | Existing frameworks for conversational question-answer generation generate a large-scale dataset based on input passages. |
| Approach: | They propose a conversational question-answer generation framework that extracts question-worthy phrases from passages and generates corresponding questions considering previous conversations. |
| Outcome: | The proposed framework improves the quality of synthetic data and can be used for domain adaptation of conversational question answering. |
DABERT: Dual Attention Enhanced BERT for Semantic Matching (2022.coling-1)
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| Challenge: | Existing models for semantic sentence matching lack the ability to capture subtle differences. |
| Approach: | They propose to use a Transformer-based pre-trained language model to capture fine-grained differences in sentence pairs by introducing a dual attention module and a fusion module to learn the aggregation of difference and affinity features. |
| Outcome: | The proposed method is able to capture fine-grained differences in sentence pairs. |
Locate Then Ask: Interpretable Stepwise Reasoning for Multi-hop Question Answering (2022.coling-1)
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| Challenge: | Existing methods for multi-hop reasoning ignore grounding on supporting facts of each step, which tends to generate inaccurate decompositions. |
| Approach: | They propose an interpretable stepwise reasoning framework that incorporates supporting sentences and questions at each intermediate step and utilizes the inference of the current hop for the next until reasoning out the final result. |
| Outcome: | The proposed model can boost performance and yield a better interpretable reasoning process without decomposition supervision. |
Less Is Better: Recovering Intended-Feature Subspace to Robustify NLU Models (2022.coling-1)
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| Challenge: | Existing approaches to debiase datasets rely on knowledge of bias attributes . current approaches focus on how to leverage kinds of supervision effectively . |
| Approach: | They propose to extend the supervision on bias by extending it into feature space. |
| Outcome: | Empirical results show that a low-dimensional subspace with intended features can represent biased datasets. |
CORN: Co-Reasoning Network for Commonsense Question Answering (2022.coling-1)
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| Challenge: | Existing work uses two independent modules to model QA content and external commonsense knowledge graph (KG) Existing research uses two separate modules to create QA contextual text representations and relationships between QA entities. |
| Approach: | They propose a commonsense question answering (QA) model that uses two independent modules to model QA contextual text representation and relationships between QA entities in KG. |
| Outcome: | The proposed model achieves state-of-the-art on QA benchmarks in the CommonsenseQA and OpenBookQA datasets. |
Logical Form Generation via Multi-task Learning for Complex Question Answering over Knowledge Bases (2022.coling-1)
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| Challenge: | Existing generation-based KBQA methods that translate natural language questions to executable logical forms are proving promising but noise introduced can lead to incorrect results. |
| Approach: | They propose a Generation-based KBQA method that uses auxiliary information to enhance logical form generation by combining unseen KB items with novel combinations. |
| Outcome: | The proposed method achieves state-of-the-art results on ComplexWebQuestions and WebQuestIONSSP datasets. |
CMQA: A Dataset of Conditional Question Answering with Multiple-Span Answers (2022.coling-1)
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| Challenge: | Existing QA datasets only contain unconditional and parallel answers . conditional question answering with hierarchical multi-span answers is challenging for the community to solve . |
| Approach: | They propose a conditional question answering task with hierarchical multi-span answers . they propose CMQA, which contains conditional and hierarchic samples . |
| Outcome: | The proposed task can be used to build more reliable and sophisticated QA systems. |
To What Extent Do Natural Language Understanding Datasets Correlate to Logical Reasoning? A Method for Diagnosing Logical Reasoning. (2022.coling-1)
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| Challenge: | Reasoning and knowledge-related skills are considered as fundamental skills for natural language understanding (NLU) tasks. |
| Approach: | They propose a method to diagnose correlations between an NLU dataset and a specific skill. |
| Outcome: | The proposed method is able to diagnose correlations between dataset and logical reasoning skill on 8 MRC and 3 NLI datasets. |
ArcaneQA: Dynamic Program Induction and Contextualized Encoding for Knowledge Base Question Answering (2022.coling-1)
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| Challenge: | Existing ranking-based KBQA models struggle with flexibility in predicting complicated queries and have impractical running time. |
| Approach: | They propose a new generation-based question answering on knowledge bases model that addresses both large search space and ambiguities in schema linking. |
| Outcome: | The proposed model overcomes two intertwined challenges on popular KBQA datasets and is highly competitive and efficient. |
Unsupervised Question Answering via Answer Diversifying (2022.coling-1)
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| Challenge: | Existing extractive question answering methods use labeled data to train QA models. |
| Approach: | They propose an unsupervised method by diversifying answers by using data construction, data augmentation and denoising filter. |
| Outcome: | The proposed method outperforms previous models on five benchmark datasets . it shows strong performance in the few-shot learning setting . |
Weakly Supervised Formula Learner for Solving Mathematical Problems (2022.coling-1)
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| Challenge: | Existing work suggests a two-phase approach to solving mathematical reasoning tasks . however, its reliance on annotated formulas as intermediate labels throughout its training limited its application. |
| Approach: | They propose a framework that allows models to learn optimal formulas autonomously with weak supervision from the final answers to mathematical problems. |
| Outcome: | The proposed framework outperforms baselines trained on incomplete yet imperfect formula annotations and weakly supervised learning methods on two representative mathematical reasoning datasets. |
Reducing Spurious Correlations for Answer Selection by Feature Decorrelation and Language Debiasing (2022.coling-1)
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| Challenge: | Existing deep neural models rely on spurious correlations between prediction labels and input features, which in general suffer from robustness and generalization. |
| Approach: | They propose a feature decorrelation module to remove feature dependencies and reduce spurious correlations by learning a weight for each instance at the training phase. |
| Outcome: | The proposed method improves the robustness of the neural ANswer selection models from the sample and feature perspectives. |
Understanding and Improving Zero-shot Multi-hop Reasoning in Generative Question Answering (2022.coling-1)
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| Challenge: | Generative question answering (QA) models generate answers to complex questions, but their mechanism for doing so is still poorly understood. |
| Approach: | They decompose multi-hop questions into multiple corresponding single-hop question chains and find marked inconsistency in QA models’ answers on these pairs of ostensibly identical question chains. |
| Outcome: | The proposed models lack zero-shot multi-hop reasoning ability when trained on single-hop questions and on logical forms. |
Domain Adaptation for Question Answering via Question Classification (2022.coling-1)
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| Challenge: | Question answering systems often experience performance deterioration upon user-generated questions. |
| Approach: | They propose a question classification framework to help QA domains adapt to different domains. |
| Outcome: | The proposed framework improves on state-of-the-art datasets against multiple datasets. |
Prompt-based Conservation Learning for Multi-hop Question Answering (2022.coling-1)
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| Challenge: | Existing multi-hop QA methods fail to answer a large fraction of sub-questions even if their parent questions are answered correctly. |
| Approach: | They propose a Prompt-based Conservation Learning framework that acquires new knowledge from multi-hop QA tasks while conserving old knowledge learned on single-hop tasks. |
| Outcome: | The proposed framework acquires new knowledge from multi-hop QA tasks while conserving old knowledge learned on single-hop tasks, mitigating forgetting. |
GLAF: Global-to-Local Aggregation and Fission Network for Semantic Level Fact Verification (2022.coling-1)
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| Challenge: | Existing fact verification models lack fine-grained reasoning over key entities . GLAF uses local fission reasoning to capture latent logical relations between clues . |
| Approach: | They propose a global-to-local fission and fissional network to capture latent logical relations hidden in multiple evidence clues. |
| Outcome: | The proposed network achieves state-of-the-art on a FEVER dataset with a 77.62% FEVER score. |
Exploiting Hybrid Semantics of Relation Paths for Multi-hop Question Answering over Knowledge Graphs (2022.coling-1)
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| Challenge: | Existing approaches to answer natural language questions on knowledge graphs (KGQA) use large-scale entity-related text corpus or knowledge graph embeddings as auxiliary information to facilitate answer selection. |
| Approach: | They propose to integrate explicit textual information and implicit KG structural features of relation paths into a novel rotate-and-scale entity link prediction framework. |
| Outcome: | The proposed method is superior to existing methods on three KGQA datasets and shows that it can be used to identify answer entities. |
Adaptive Threshold Selective Self-Attention for Chinese NER (2022.coling-1)
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| Challenge: | Named entity recognition (NER) is a computationally difficult task in Chinese since there is no natural delimiter between words in sentences. |
| Approach: | They propose a data-driven Adaptive Threshold Selective Self-Attention mechanism to select the most relevant characters to enhance Transformer architecture for Chinese named entity recognition. |
| Outcome: | Experiments on four benchmark Chinese NER datasets show the proposed mechanism improves performance. |
Cluster-aware Pseudo-Labeling for Supervised Open Relation Extraction (2022.coling-1)
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| Challenge: | Existing methods to extract novel relations do not achieve effective knowledge transfer . experimental results show that the proposed method is state-of-the-arts . |
| Approach: | They propose a Cluster-aware Pseudo-Labeling method to improve pseudo-labels quality . they firstly pre-trained the relation models with pre-defined relations to learn them . |
| Outcome: | The proposed method improves the pseudo-labels quality and transfer more knowledge for discovering novel relations. |
Few-shot Named Entity Recognition with Entity-level Prototypical Network Enhanced by Dispersedly Distributed Prototypes (2022.coling-1)
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| Challenge: | Existing prototypical networks for named entity recognition suffer from label dependency and tightly distributed prototypes, thus causing misclassifications. |
| Approach: | They propose an Entity-level Prototypical Network enhanced by dispersedly distributed prototypes to build entity-level prototypes and distribute them dispersionally. |
| Outcome: | The proposed system outperforms the previous models on two evaluation tasks and the Few-NERD settings in terms of overall performance. |
Different Data, Different Modalities! Reinforced Data Splitting for Effective Multimodal Information Extraction from Social Media Posts (2022.coling-1)
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| Challenge: | Recent multimodal information extraction approaches overestimate the significance of images. |
| Approach: | They propose a general data splitting strategy to divide social media posts into two sets to achieve better performance under information extraction models of the corresponding modalities. |
| Outcome: | The proposed method outperforms existing models on two different multimodal information extraction tasks. |
Augmentation, Retrieval, Generation: Event Sequence Prediction with a Three-Stage Sequence-to-Sequence Approach (2022.coling-1)
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| Challenge: | Existing methods to predict event sequences are complex and ignore the knowledge of external events. |
| Approach: | They propose a statistical induction problem to generate a sequence of events by exploring the similarity between the given goal and known sequences of events. |
| Outcome: | The proposed model outperforms existing methods on an event sequence prediction task. |
Generating Temporally-ordered Event Sequences via Event Optimal Transport (2022.coling-1)
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| Challenge: | Existing methods for temporal event ordering and event infilling ignore the global semantics of events, and the model adopts a word-level objective to model events in texts. |
| Approach: | They propose a temporal event ordering and event infilling task using a model that uses maximum likelihood estimation to model events in texts. |
| Outcome: | The proposed model outperforms existing models on all evaluation datasets. |
Improving Continual Relation Extraction through Prototypical Contrastive Learning (2022.coling-1)
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| Challenge: | Continual relation extraction (CRE) aims to extract relations towards the continuous and iterative arrival of new data, of which the major challenge is the catastrophic forgetting of old tasks. |
| Approach: | They propose a Continual Relation Extraction framework with Contrastive Learning which is built with a classification network and a prototypical contrastive network to achieve incremental-class learning of CRE. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on two public datasets and proves its effectiveness on improving performance. |
Prompt-based Text Entailment for Low-Resource Named Entity Recognition (2022.coling-1)
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| Challenge: | Pre-trained Language Models (PLMs) have been applied in NLP tasks but require labeled data for downstream tasks. |
| Approach: | They propose a method for low-resource named entity recognition that uses prompts to get entailment scores for each candidate and inject tagging labels into prompts. |
| Outcome: | The proposed method achieves competitive performance on the CoNLL03 dataset, and better than fine-tuned counterparts on the MIT Movie and Few-NERD datasets in low-resource settings. |
Key Mention Pairs Guided Document-Level Relation Extraction (2022.coling-1)
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| Challenge: | Document-level Relation Extraction (DocRE) aims to identify the relations between entities in a given document. |
| Approach: | They propose a document-level relation extraction model with two modules to model mention-level relations. |
| Outcome: | The proposed model outperforms existing state-of-the-art models on two public DocRE datasets and outperformed existing models. |
A Hybrid Model of Classification and Generation for Spatial Relation Extraction (2022.coling-1)
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| Challenge: | Existing studies only focus on spatial relations extraction as a classification task . spatial information is one kind of critical information for natural language understanding . |
| Approach: | They propose a hybrid model that generates null-role relations and extracts non-null-rol . they propose varying kinds of schemes to represent spatial relation . |
| Outcome: | The proposed model outperforms the baselines on the spatial relation extraction task on SpaceEval. |
Mining Health-related Cause-Effect Statements with High Precision at Large Scale (2022.coling-1)
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| Challenge: | Existing methods for assessing the health relatedness of phrases and sentences are slower and less effective than state-of-the-art medical entity linkers. |
| Approach: | They propose a termhood score that achieves 69% recall at over 90% precision on a web dataset with cause-effect statements. |
| Outcome: | The proposed method achieves 69% recall at over 90% precision on a web dataset with cause-effect statements. |
Find the Funding: Entity Linking with Incomplete Funding Knowledge Bases (2022.coling-1)
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| Challenge: | Existing approaches to identifying and linking funding entities are suboptimal for the funding domain. |
| Approach: | They propose an entity linking model that can perform NIL prediction and overcome data scarcity issues in a time and data-efficient manner. |
| Outcome: | The proposed model outperforms existing baselines and overcomes data scarcity issues in a time and data-efficient manner. |
KiPT: Knowledge-injected Prompt Tuning for Event Detection (2022.coling-1)
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| Challenge: | Existing prompt-based methods may suffer from low precision because they lack event-related semantic knowledge. |
| Approach: | They propose a Knowledge-injected Prompt Tuning model to improve prompt tuning . event detection aims to detect events from text by identifying and classifying event triggers . |
| Outcome: | The proposed model outperforms baseline models in few-shot scenarios. |
OneEE: A One-Stage Framework for Fast Overlapping and Nested Event Extraction (2022.coling-1)
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| Challenge: | Event extraction (EE) is an essential task of information extraction, which aims to extract structured event information from unstructured text. |
| Approach: | They propose a tagging scheme and a model to form EE as word-word relation recognition using parallel grid tapping. |
| Outcome: | The proposed model achieves state-of-the-art on 3 overlapped and nested EE benchmarks and faster than baselines. |
Joint Language Semantic and Structure Embedding for Knowledge Graph Completion (2022.coling-1)
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| Challenge: | Existing methods to complete knowledge triplets rely on structures or semantics, but use semantics to improve performance. |
| Approach: | They propose to embed semantics in the natural language description of knowledge triplets with their structure information. |
| Outcome: | The proposed method improves performance on knowledge graph benchmarks and on low-resource regimes. |
Event Detection with Dual Relational Graph Attention Networks (2022.coling-1)
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| Challenge: | Existing approaches to event detection confuse syntactic relations and introduce redundant or noisy information, leading to performance degradation. |
| Approach: | They propose a model that exploits syntactic and semantic relations to alleviate the problem by combining syntatic and semantic knowledge. |
| Outcome: | The proposed model outperforms state-of-the-art methods on a ACE2005 benchmark dataset. |
A Multi-Format Transfer Learning Model for Event Argument Extraction via Variational Information Bottleneck (2022.coling-1)
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| Challenge: | Event argument extraction (EAE) aims to extract arguments with given roles from texts. |
| Approach: | They propose a multi-format transfer learning model with variational information bottleneck to learn from existing datasets. |
| Outcome: | The proposed model improves on three benchmark datasets and obtains state-of-the-art performance on EAE. |
RSGT: Relational Structure Guided Temporal Relation Extraction (2022.coling-1)
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| Challenge: | Temporal relation extraction (TRE) is crucial for natural language understanding. |
| Approach: | They propose a Temporal Relational Structure Guided Temporal Relations Extraction task to extract relational structure features that can fit for both inter-sentence and intra-sentent relations. |
| Outcome: | The proposed method improves on two well-known datasets, MATRES and TB-Dense, and can be used for clinical diagnosis and summarization. |
Learning Hierarchy-Aware Quaternion Knowledge Graph Embeddings with Representing Relations as 3D Rotations (2022.coling-1)
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| Challenge: | Existing knowledge graph embedding models fail to model semantic hierarchies . Existing methods fail to understand the semantic hierarchies of knowledge graphs . |
| Approach: | They propose a model which embeds entities as pure quaternions and constrains the modulus of entities to make them have hierarchical distributions. |
| Outcome: | The proposed model can encode symmetry/antisymmetry, inversion, composition, multiple relation patterns and learn semantic hierarchies simultaneously. |
Two Languages Are Better than One: Bilingual Enhancement for Chinese Named Entity Recognition (2022.coling-1)
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| Challenge: | Existing studies focus on internal features of Chinese named entity recognition, but neglect other lingual modalities. |
| Approach: | They propose a bilingual enhancement module for Chinese Named Entity Recognition . they integrate rich English information into Chinese representation and use it to learn the interaction between bilinguals and dependent information within Chinese. |
| Outcome: | The proposed model can learn the interaction of bilinguals and dependent information within Chinese. |
Read Extensively, Focus Smartly: A Cross-document Semantic Enhancement Method for Visual Documents NER (2022.coling-1)
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| Challenge: | Existing methods to extract entities from visually-rich documents ignore the inherent multimodality of VRDs and thus the suboptimal results are achieved. |
| Approach: | They propose a multimodal semantic enhancement method that filters redundant information in the current document and a cross-document information awareness technique to enrich the entity-related context. |
| Outcome: | The proposed method outperforms existing methods on two documents understanding benchmarks covering eight languages. |
STAD: Self-Training with Ambiguous Data for Low-Resource Relation Extraction (2022.coling-1)
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| Challenge: | Existing approaches for low-resource relation extraction use only confident instances and uncertain instances. |
| Approach: | They propose a self-training approach for low-resource relation extraction using auto-annotated instances. |
| Outcome: | The proposed method improves on two widely used datasets with low-resource settings. |
Flat Multi-modal Interaction Transformer for Named Entity Recognition (2022.coling-1)
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| Challenge: | Named entity recognition (MNER) aims at identifying entity spans and recognizing their categories in social media posts with the aid of images. |
| Approach: | They propose to use sentences and general domain words to obtain visual cues to transform the fine-grained semantic representation of vision and text into a unified lattice structure and leverage entity boundary detection as an auxiliary task to alleviate visual bias. |
| Outcome: | The proposed method achieves state-of-the-art on two benchmark datasets. |
MetaSLRCL: A Self-Adaptive Learning Rate and Curriculum Learning Based Framework for Few-Shot Text Classification (2022.coling-1)
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| Challenge: | Existing few-shot text classification methods lack labeled data in many scenarios. |
| Approach: | They propose a meta learning framework that obtains different learning rates for different tasks and neural network layers to enable the meta learner to quickly adapt to new training data. |
| Outcome: | The proposed framework can obtain different learning rates for different tasks and neural network layers so as to enable the meta learner to quickly adapt to new tasks. |
A Simple Temporal Information Matching Mechanism for Entity Alignment between Temporal Knowledge Graphs (2022.coling-1)
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| Challenge: | Existing methods for EA between temporal KGs incorporate relational and temporal information into entity embeddings. |
| Approach: | They propose a method to generate unsupervised alignment seeds using temporal information from TKGs. |
| Outcome: | The proposed method outperforms the previous methods by using temporal information. |
DCT-Centered Temporal Relation Extraction (2022.coling-1)
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| Challenge: | Existing work on temporal relation extraction focuses on extracting temporal relations between events . previous work on relation extraction focused on focusing on event-centered tasks . |
| Approach: | They propose a temporal relation extraction model that unifies events, timexes and DCT . they propose combining event mentions, time expressions and document creation time into a sentence-style model . |
| Outcome: | The proposed model outperforms baselines on E-E, E-T and E-D significantly. |
Document-level Biomedical Relation Extraction Based on Multi-Dimensional Fusion Information and Multi-Granularity Logical Reasoning (2022.coling-1)
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| Challenge: | Existing models with reasoning are single-granularity based on one element information, ignoring complementary fact of different granularities. |
| Approach: | They propose a document-level biomedical relation extraction model called FILR . it uses multi-dimensional information fusion and multi-granularity logic to obtain rich inferences . |
| Outcome: | The proposed model extracts all relation facts from biomedical documents . it is based on multi-dimensional information fusion and multi-granularity logic reasoning . the proposed model achieves state-of-the-art performance on two widely used biomedically corpora . |
Simple Yet Powerful: An Overlooked Architecture for Nested Named Entity Recognition (2022.coling-1)
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| Challenge: | Named Entity Recognition (NER) is an important task in Natural Language Processing that aims to identify text spans belonging to predefined categories. |
| Approach: | They propose to revisit the Multiple LSTM-CRF (MLC) model, a simple, overlooked, yet powerful approach based on training independent sequence labeling models for each entity type. |
| Outcome: | The proposed model achieves state-of-the-art results in the Chilean Waiting List corpus by including pre-trained language models. |
ERGO: Event Relational Graph Transformer for Document-level Event Causality Identification (2022.coling-1)
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| Challenge: | Existing methods to identify event-event causal relations in a document are noisy and require heuristic rules or external tools. |
| Approach: | They propose a document-level event-event causality identification framework that uses heuristic rules to design edges between events. |
| Outcome: | The proposed framework outperforms existing state-of-the-art methods on two benchmark datasets. |
DRK: Discriminative Rule-based Knowledge for Relieving Prediction Confusions in Few-shot Relation Extraction (2022.coling-1)
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| Challenge: | Existing methods to identify relation type in low-resource scenario fall into prediction confusions owing to the limited inference ability over shallow text features. |
| Approach: | They propose a discriminative rule-based knowledge method to identify the relation type between entities in a given text in the low-resource scenario. |
| Outcome: | The proposed method improves on four types of meta tasks with a 6.0% accuracy gain on average. |
DocQueryNet: Value Retrieval with Arbitrary Queries for Form-like Documents (2022.coling-1)
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| Challenge: | Existing methods that only address a fixed set of fields are difficult to use for different form types. |
| Approach: | They propose a value retrieval method with arbitrary queries for form-like documents . they propose 'docQueryNet' to predict target value based on understanding of layout and semantics of a form . |
| Outcome: | The proposed method outperforms existing methods on value retrieval . it improves document understanding on large-scale model pre-training by 17% . |
DoSEA: A Domain-specific Entity-aware Framework for Cross-Domain Named Entity Recogition (2022.coling-1)
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| Challenge: | Existing approaches to named entity recognition ignore domain-specific information and suffer from subtype conflicts. |
| Approach: | They propose a machine reading comprehension framework which can identify domain-specific semantic differences and mitigate the subtype conflicts between domains. |
| Outcome: | The proposed framework can identify domain-specific semantic differences and mitigate the subtype conflicts between domains. |
Incremental Prompting: Episodic Memory Prompt for Lifelong Event Detection (2022.coling-1)
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| Challenge: | Existing methods to improve lifelong event detection performance are limited by the limited stored examples. |
| Approach: | They propose to use Episodic Memory Prompts to explicitly retain the learned task-specific knowledge. |
| Outcome: | The proposed method can be used to update a model with new event types while retaining the capability on previously learned types. |
Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expect (2022.coling-1)
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| Challenge: | text-to-SQL is a language processing and database-based language processing (NLP) task is to convert natural utterances into SQL queries and its practical application is to build natural language interfaces to database systems. |
| Approach: | They propose to conduct a systematic survey of text-to-SQL to examine the challenges and potential future directions. |
| Outcome: | The proposed system converts natural utterances into SQL queries and is a representative task in semantic parsing. |
An MRC Framework for Semantic Role Labeling (2022.coling-1)
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| Challenge: | Existing work on semantic role labels ignores the semantic connection between the two tasks . et al. (2010) defined two types of semantic roles: core roles and non-core roles. |
| Approach: | They propose to use machine reading comprehension to bridge the gap between these two tasks . they formalize predicate disambiguation as multiple-choice machine reading understanding . |
| Outcome: | The proposed framework achieves state-of-the-art or comparable results to previous work . it uses the descriptions of candidate senses of a given predicate as options to select the correct sense . |
PCBERT: Parent and Child BERT for Chinese Few-shot NER (2022.coling-1)
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| Challenge: | Existing approaches to improve model performance on few-shot or zero-shot datasets are not effective for Chinese few- shot NER. |
| Approach: | They propose a prompt-based Parent and Child BERT for Chinese few-shot NER to train an annotating model on high-resource datasets and then discover more implicit labels on low-resourced datasets. |
| Outcome: | The proposed model can be used on Weibo and other Chinese NER datasets and it is shown to be effective in few-shot learning. |
Label Smoothing for Text Mining (2022.coling-1)
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| Challenge: | Existing text mining models are trained with 0-1 hard label that indicates whether an instance belongs to a class, ignoring rich information of the relevance degree. |
| Approach: | They propose a keyword-based method to automatically generate soft labels from hard labels . they exploit relevance between labels and instances to incorporate them into models . |
| Outcome: | The proposed method improves models under balanced and unbalanced conditions. |
Diverse Multi-Answer Retrieval with Determinantal Point Processes (2022.coling-1)
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| Challenge: | Existing open domain question answering systems provide a single answer to ambiguous questions. |
| Approach: | They propose a re-ranking approach that takes query-passage relevance and passage-passance correlation into account to retrieve passages that are query-relevant and diverse. |
| Outcome: | The proposed method outperforms state-of-the-art on the AmbigQA dataset. |
Improving Deep Embedded Clustering via Learning Cluster-level Representations (2022.coling-1)
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| Challenge: | Existing efforts to learn meaningful representations at the instance level are limited. |
| Approach: | They propose a deep embedded clustering model with cluster-level representation learning to jointly learn cluster and instance level representations. |
| Outcome: | The proposed model produces meaningful clusters on real-world short text datasets. |
Decoupling Mixture-of-Graphs: Unseen Relational Learning for Knowledge Graph Completion by Fusing Ontology and Textual Experts (2022.coling-1)
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| Challenge: | Existing methods for knowledge Graph Completion (KGC) fail in unseen relation representations. |
| Approach: | They propose to use three kinds of graphs to obtain unseen relation representations . they propose to decouple mixture-of-graph experts (DMoG) for unseened relations learning . |
| Outcome: | The proposed method outperforms the state-of-the-art methods on unseen relation representations. |
CETA: A Consensus Enhanced Training Approach for Denoising in Distantly Supervised Relation Extraction (2022.coling-1)
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| Challenge: | Existing methods for relation extraction use noisy instances and poor quality training data. |
| Approach: | They propose a sentence-level DSRE method that denies noisy samples from the wrong classification space on the feature space by enhancing the classification consensus between two discrepant classifiers. |
| Outcome: | The proposed method outperforms existing methods on widely-used benchmarks and significantly outperformed existing methods. |
MedDistant19: Towards an Accurate Benchmark for Broad-Coverage Biomedical Relation Extraction (2022.coling-1)
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| Challenge: | Relation extraction in the biomedical domain is challenging due to the lack of labeled data and high annotation costs. |
| Approach: | They propose to use distant supervision to pair knowledge graph relationships with raw texts to tackle the scarcity of annotated data and to validate their results. |
| Outcome: | The proposed benchmarks are more accurate and consistent with existing benchmarks and show that there is no train-test leakage. |
Decorrelate Irrelevant, Purify Relevant: Overcome Textual Spurious Correlations from a Feature Perspective (2022.coling-1)
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| Challenge: | Existing methods to debiase samples with biased features obstructs the model in learning from non-biased parts of the samples. |
| Approach: | They propose to eliminate spurious correlations in a fine-grained manner from a feature space perspective by using Random Fourier Features and weighted re-sampling to decorrelate dependencies between features. |
| Outcome: | The proposed method eliminates spurious correlations in a fine-grained manner from a feature space perspective. |
Event Causality Identification via Derivative Prompt Joint Learning (2022.coling-1)
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| Challenge: | Existing methods for event causality identification lack annotated data, and they lack the ability to identify explicit and implicit causality. |
| Approach: | They propose a derivative prompt joint learning model which leverages potential causal knowledge in the pre-trained language model to tackle the data scarcity problem. |
| Outcome: | The proposed model can identify explicit and implicit causality on two benchmark datasets and it has great advantages over previous methods. |
Event Causality Extraction with Event Argument Correlations (2022.coling-1)
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| Challenge: | Event Causality Identification (ECI) ignores crucial event structure and cause-effect component information, making it struggle for downstream applications. |
| Approach: | They propose a task to extract event causality pairs with their structured event information from plain text. |
| Outcome: | The proposed method captures the intra- and inter-event argument correlations for ECE and provides several future directions. |
SCL-RAI: Span-based Contrastive Learning with Retrieval Augmented Inference for Unlabeled Entity Problem in NER (2022.coling-1)
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| Challenge: | Existing methods to solve Unlabeled Entity Problem (UEP) in Named Entities Recognition datasets are not effective in real-world datasets. |
| Approach: | They propose to decrease the distance of span representations with the same label while increasing it for different ones via span-based contrastive learning. |
| Outcome: | The proposed method outperforms the previous method on two real-world datasets. |
A Relation Extraction Dataset for Knowledge Extraction from Web Tables (2022.coling-1)
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| Challenge: | Existing datasets with relational web-tables are either synthetic, or very small in size. |
| Approach: | They propose to annotate relational web-tables against a human-annotated dataset using crowd sourced annotators from MTurk. |
| Outcome: | The proposed dataset has 50x larger number of column pairs than the existing human-annotated benchmark. |
Automatic Keyphrase Generation by Incorporating Dual Copy Mechanisms in Sequence-to-Sequence Learning (2022.coling-1)
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| Challenge: | Existing models for keyphrase generation use a copy mechanism to generate keyphrases, but they do not identify key words in the source text and copy them to create more keyphrase. |
| Approach: | They propose a dual-copier keyphrase generation model that uses a sequence-to-sequence model to generate keyphrases for a piece of text. |
| Outcome: | The proposed model outperforms baseline models and achieves an obvious performance improvement. |
Dependency-aware Prototype Learning for Few-shot Relation Classification (2022.coling-1)
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| Challenge: | Existing methods for few-shot relation classification fail to distinguish multiple relations that co-exist in one sentence. |
| Approach: | They propose a dependency-aware prototype learning method for few-shot relation classification . they utilize dependency trees and shortest dependency paths as structural information . |
| Outcome: | The proposed method achieves better performance than baselines on the FewRel dataset. |
MECI: A Multilingual Dataset for Event Causality Identification (2022.coling-1)
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| Challenge: | Event Causality Identification (ECI) is a task of detecting causal relations between events mentioned in text. |
| Approach: | They propose a multilingual dataset that provides consistent annotations for event causality relations in five languages. |
| Outcome: | The proposed dataset provides consistent annotation guidelines for five languages . the dataset can provide ample research challenges and directions for future research . |
Method Entity Extraction from Biomedical Texts (2022.coling-1)
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| Challenge: | Scientific research papers consist of complex keywords and domain-specific terminologies, and new terminologie erupt. |
| Approach: | They find method terminologies in biomedical text using rule-based and machine learning techniques . authors propose to use a silver standard corpus to extract method entities from biomedically text . |
| Outcome: | The proposed method entities can be extracted from biomedical text with reasonable accuracy . the proposed method entity extraction method is based on a rule-based method and a machine learning technique. |
Optimal Partial Transport Based Sentence Selection for Long-form Document Matching (2022.coling-1)
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| Challenge: | Existing methods for document matching are limited by the partial nature of the sentence-level matching signals. |
| Approach: | They propose a matching approach that equips existing document matching models with an Optimal Partial Transport component, namely OPT-Match, which selects the key sentences that play a major role in matching. |
| Outcome: | The proposed approach outperforms existing models on four publicly available datasets and the key sentences selected by it are consistent with human-provided rationales. |
LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Prompting (2022.coling-1)
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Xiang Chen, Lei Li, Shumin Deng, Chuanqi Tan, Changliang Xu, Fei Huang, Luo Si, Huajun Chen, Ningyu Zhang
| Challenge: | Existing approaches for Named Entity Recognition (NER) use extensive labeled data for model training, which struggles in low-resource scenarios. |
| Approach: | They propose a lightweight tuning paradigm for low-resource NER via pluggable prompting . they construct a learnable verbalizer of entity categories without any label-specific classifiers . |
| Outcome: | The proposed model outperforms baselines and class transfer models in low-resource scenarios. |
Cross-modal Contrastive Attention Model for Medical Report Generation (2022.coling-1)
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| Challenge: | Existing methods for medical report generation are unable to capture useful information from historical cases. |
| Approach: | They propose a model that captures both visual and semantic information from similar cases. |
| Outcome: | The proposed model outperforms the state-of-the-art models on almost all metrics on IU X-Ray and MIMIC-CXR benchmarks. |
Domain-Specific NER via Retrieving Correlated Samples (2022.coling-1)
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| Challenge: | Successful Named Entity Recognition models fail on texts from some special domains, for example, Chinese addresses and e-commerce titles. |
| Approach: | They propose to enhance NER models with correlated samples to help the text understanding . they draw correlated texts by the sparse BM25 retriever from large-scale in-domain unlabeled data . |
| Outcome: | Empirical results show that NER models can be enhanced with correlated samples . the proposed model can be used to reason out the correct answer on hard cases . |
Type-enriched Hierarchical Contrastive Strategy for Fine-Grained Entity Typing (2022.coling-1)
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| Challenge: | Experimental results show that fine-grained entity typing (FET) can be used to deduce specific semantic types of entities. |
| Approach: | They propose a type-enriched hierarchical contrastive strategy to model type differences . their method can make type information directly perceptible and improve distinguishability . |
| Outcome: | The proposed method can model the differences between hierarchical types and distinguish multi-grained similar types at different granularities. |
Document-Level Relation Extraction via Pair-Aware and Entity-Enhanced Representation Learning (2022.coling-1)
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| Challenge: | Existing document-level relation extraction methods are sparse in relational entity pairs and the representation of entity pairs is insufficient. |
| Approach: | They propose a Pair-Aware and Entity-Enhanced(PAEE) model to solve two challenges . they propose predicting potential relational entity pairs and assembling directional entity pairs . |
| Outcome: | The proposed model can obtain state-of-the-art performance on four benchmark datasets . it can predict potential relational entity pairs and assemble directional entity pairs . |
Improving Zero-Shot Entity Linking Candidate Generation with Ultra-Fine Entity Type Information (2022.coling-1)
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| Challenge: | Entity linking is a task of assigning entity mentions to referent entities in a knowledge base. |
| Approach: | They propose to use ultra-fine-grained type information to improve the generalization ability of EL models by utilizing a low-level task to extract ultra-finish entity type information. |
| Outcome: | The proposed model achieves state-of-the-art in the zero-shot entity linking task . |
CofeNet: Context and Former-Label Enhanced Net for Complicated Quotation Extraction (2022.coling-1)
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| Challenge: | Existing solutions for quotation extraction use rule-based approaches and sequence labeling models. |
| Approach: | They propose a Context and Former-Label Enhanced Net for quotation extraction. |
| Outcome: | The proposed method achieves state-of-the-art performance on complicated quotation extraction on two public datasets and one proprietary dataset. |
Supporting Medical Relation Extraction via Causality-Pruned Semantic Dependency Forest (2022.coling-1)
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| Challenge: | Medical relation extraction (MRE) tasks aims to extract relations between entities in medical literature. |
| Approach: | They propose to combine semantic and syntactic information from medical texts by using causal explanation theory. |
| Outcome: | Empirically, the proposed model outperforms existing methods on benchmark medical datasets. |
Aspect-based Sentiment Analysis as Machine Reading Comprehension (2022.coling-1)
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| Challenge: | Existing approaches to aspect-based sentiment analysis stack multiple modules and result in severe error propagation. |
| Approach: | They propose a MRC-PrOmpt mOdeL framework where multiple sentiment aspects are elicited by a machine reading comprehension model and their corresponding sentiment polarities are classified in a prompt learning way. |
| Outcome: | The proposed framework significantly outperforms existing state-of-the-art models or achieves comparable performance on widely-used benchmark datasets. |
Nested Named Entity Recognition as Corpus Aware Holistic Structure Parsing (2022.coling-1)
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| Challenge: | Named entity recognition is a natural language processing task . nested NER is based on a linear structure, but there is no research on applying corpus-level information to NER. |
| Approach: | They propose a holistic structure parsing algorithm to reveal the entire NEs in a sentence . they introduce points-wise mutual information and other frequency features from corpus-aware statistics . |
| Outcome: | The proposed model outperforms existing models on widely-used benchmarks and achieves state-of-the-art. |
DESED: Dialogue-based Explanation for Sentence-level Event Detection (2022.coling-1)
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Yinyi Wei, Shuaipeng Liu, Jianwei Lv, Xiangyu Xi, Hailei Yan, Wei Ye, Tong Mo, Fan Yang, Guanglu Wan
| Challenge: | Existing methods for sentence-level event detection depend on manual annotations or domain expertise to design sophisticated templates and rules. |
| Approach: | They propose a dialogue-based explanation paradigm to enhance sentence semantics for event detection. |
| Outcome: | The proposed method can be applied to two event detection datasets. |
Data Augmentation for Few-Shot Knowledge Graph Completion from Hierarchical Perspective (2022.coling-1)
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| Challenge: | Existing knowledge graph completion models require only a few associative triples to complete a relationship. |
| Approach: | They propose to perform data augmentation from two perspectives to solve the FKGC problem by inferring new triple facts from existing models. |
| Outcome: | The proposed framework can be applied to a number of existing models. |
CLIO: Role-interactive Multi-event Head Attention Network for Document-level Event Extraction (2022.coling-1)
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| Challenge: | Existing methods for document-level event extraction struggle due to two intrinsic challenges: nested arguments and multiple events. |
| Approach: | They propose a role-interactive multi-event head attention network to solve two challenges . they map different events to multiple subspaces and then determine whether the current event exists . |
| Outcome: | The proposed model improves on two widely used DEE datasets on the Internet. |
COPNER: Contrastive Learning with Prompt Guiding for Few-shot Named Entity Recognition (2022.coling-1)
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| Challenge: | Existing methods for Named Entity Recognition (NER) use a similarity metric to measure semantic similarity between test samples and referents, but their performance is limited due to the label scarcity. |
| Approach: | They propose a novel approach to learn a similarity metric for measuring the semantic similarity between test samples and referents, where each referent represents an entity class. |
| Outcome: | The proposed approach outperforms state-of-the-art models with a significant margin in most cases. |
Few Clean Instances Help Denoising Distant Supervision (2022.coling-1)
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| Challenge: | Existing distantly supervised entity relation extractors rely on noisy data for training and evaluation. |
| Approach: | They propose a criterion for clean instance selection based on influence functions to collect sample-level evidence for recognizing good instances. |
| Outcome: | The proposed method shows strong performance on real and synthetic noisy datasets. |
SEE-Few: Seed, Expand and Entail for Few-shot Named Entity Recognition (2022.coling-1)
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| Challenge: | Existing few-shot named entity recognition methods focus on leveraging existing datasets in the rich-resource domains which might fail in training-from-scratch setting. |
| Approach: | They propose a multi-task learning framework for Few-shot named entity recognition without using source domain data. |
| Outcome: | The proposed framework outperforms state-of-the-art few-shot named entity recognition methods on a training-from-scratch dataset. |
Ruleformer: Context-aware Rule Mining over Knowledge Graph (2022.coling-1)
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| Challenge: | Existing work on rule mining focuses on mining rules, but how to select appropriate rules for completion of different triplets has not been discussed. |
| Approach: | They propose to take context information into consideration when selecting suitable rules . they devise a transformer-based rule mining approach, Ruleformer . |
| Outcome: | The proposed model takes context information into consideration, which helps select suitable rules for inference tasks. |
Are People Located in the Places They Mention in Their Tweets? A Multimodal Approach (2022.coling-1)
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| Challenge: | Experimental results show that a neural architecture that combines both modalities yields better results. |
| Approach: | They propose a neural architecture that combines both modalities to solve the problem of determining whether people are located in tweets. |
| Outcome: | The proposed model combines both modalities to produce better results . |
Multi-modal Contrastive Representation Learning for Entity Alignment (2022.coling-1)
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| Challenge: | Existing studies focus on how to utilize information from different modalities, but it is not trivial to leverage multi-modal knowledge in entity alignment because of the modality heterogeneity. |
| Approach: | They propose a Multi-modal Contrastive Learning based Entity Alignment model which learns multiple individual representations from multiple modalities and performs contrastive learning to jointly model inter-modal and inter-modal interactions. |
| Outcome: | The proposed model outperforms state-of-the-art models on public datasets under both supervised and unsupervised conditions. |
Nonparametric Forest-Structured Neural Topic Modeling (2022.coling-1)
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| Challenge: | Existing hierarchical neural topic models can only extract topics at the same level. |
| Approach: | They propose to use self-attention mechanism to capture parent-child topic relationships and build a sparse directed acyclic graph to form a topic forest. |
| Outcome: | The proposed model outperforms baseline models on topic hierarchical rationality and affinity. |
KGE-CL: Contrastive Learning of Tensor Decomposition Based Knowledge Graph Embeddings (2022.coling-1)
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| Challenge: | Existing knowledge graph embedding methods ignore semantic similarity between related entities and entity-relation couples in different triples . |
| Approach: | They propose a contrastive learning framework for tensor decomposition based (TDB) KGE that can shorten the semantic distance of related entities and entity-relation couples in different triples and thus improve the performance of KGE. |
| Outcome: | The proposed method achieves 51.2% MRR, 46.8% Hits@1 on three standard KGE datasets, 37.8% MRR and 28.6% Hits @1 on FB15k-237 datasets and 59.1% MRR . |
A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection (2022.coling-1)
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| Challenge: | Existing methods for fake news detection focus on fact-checked reports, resulting in limited coverage and debunking delays. |
| Approach: | They propose a Coarse-to-fine Cascaded Evidence-Distillation neural network for explainable fake news detection based on raw reports . they use hierarchical encoders and cascaded selectors to select most explainable sentences for verdicts on top of selected top-K reports based upon raw reports. |
| Outcome: | The proposed model outperforms baseline detection methods and generates high-quality explanations from diverse evaluation perspectives. |
Document-level Event Factuality Identification via Machine Reading Comprehension Frameworks with Transfer Learning (2022.coling-1)
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| Challenge: | Document-level Event Factuality Identification (DEFI) is a fundamental and crucial task in NLP. |
| Approach: | They propose a framework for document-level event factuality identification (DEFI) they propose to use Span-Extraction and Multiple-Choice to model DEFI as machine reading comprehension tasks . |
| Outcome: | The proposed model outperforms state-of-the-art models on a document-based event factuality task . it uses Span-Extraction (Ext) and Multiple-Choice (Mch) knowledge to extract knowledge from large-scale MRC corpus . |
Unregulated Chinese-to-English Data Expansion Does NOT Work for Neural Event Detection (2022.coling-1)
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| Challenge: | Experimental results show that cross-language data expansion results in performance degradation. |
| Approach: | They leverage cross-language data expansion and retraining to enhance neural Event Detection on English ACE corpus. |
| Outcome: | The proposed method improves ED performance by 1.6% over the straight data combination. |
Finding Influential Instances for Distantly Supervised Relation Extraction (2022.coling-1)
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| Challenge: | Distant supervision models suffer from high label noise and are not reliable for DS. |
| Approach: | They propose a model-agnostic instance sampling method for relation extraction (RE) by influence function, namely REIF. |
| Outcome: | The proposed method reduces the computational complexity from O(mn) to O(1), with analyzing its robustness on the selected sampling function. |
A Simple Model for Distantly Supervised Relation Extraction (2022.coling-1)
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| Challenge: | Recent methods focus on exploiting bag representations with complex de-noising scheme to achieve remarkable performance. |
| Approach: | They propose a BERT-based Graph convolutional network model that exploits bag representations . their model extracts key information from each instance and constructs a bag graph . |
| Outcome: | The proposed model improves on two benchmark datasets, i.e., NYT10 and GDS. |
Augmenting Legal Judgment Prediction with Contrastive Case Relations (2022.coling-1)
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| Challenge: | Existing legal judgment prediction methods only consider one case fact description as input, which may not fully utilize information in the data such as case relations and frequency. |
| Approach: | They propose a new perspective that introduces some contrastive case relations to construct case triples as input and a corresponding judgment prediction framework with case triple modeling. |
| Outcome: | The proposed framework can be used to refine encoding and decoding processes using three customized modules on two public datasets. |
Constrained Regeneration for Cross-Lingual Query-Focused Extractive Summarization (2022.coling-1)
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Elsbeth Turcan, David Wan, Faisal Ladhak, Petra Galuscakova, Sukanta Sen, Svetlana Tchistiakova, Weijia Xu, Marine Carpuat, Kenneth Heafield, Douglas Oard, Kathleen McKeown
| Challenge: | Query-focused summarization of foreign-language documents can help a user understand whether a document is relevant to a query term. |
| Approach: | They propose to use machine translation and post-editing to improve human relevance judgments . they include a query term in a summary when its translation appears in the source document . |
| Outcome: | The proposed approach improves human relevance judgments by including a query term in a summary when its translation appears in the source document. |
Programmable Annotation with Diversed Heuristics and Data Denoising (2022.coling-1)
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| Challenge: | Neural natural language generation and understanding models require massive amounts of annotated data to be competitive. |
| Approach: | They propose a data programming framework that can jointly construct labeled data for language generation and understanding tasks by allowing annotators to modify an automatically-inferred alignment rule set between sequence labels and text. |
| Outcome: | The proposed framework generates high-quality data within a 1.48 BLEU and 6.42 slot F1 of 100% human-labeled data with just 100 labeled data samples outperforming benchmark annotation frameworks and other semi-supervised approaches. |
Text-to-Text Extraction and Verbalization of Biomedical Event Graphs (2022.coling-1)
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| Challenge: | Biomedical events represent complex, graphical, and semantically rich interactions expressed in the scientific literature. |
| Approach: | They propose a framework to solve event extraction and event verbalization with a unified text-to-text approach. |
| Outcome: | The proposed framework achieves greater state-of-the-art performance than single-task competitors and can generate coherent natural language utterances from structured data. |
Multimodal Semi-supervised Learning for Disaster Tweet Classification (2022.coling-1)
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| Challenge: | During natural disasters, people use social media platforms to post information about casualties and damage . annotating data can be burdensome, subjective and expensive . et al., 2018b; sohn e.t., 2020) proposed semi-supervised multimodal approach to improve performance on multimodal tasks. |
| Approach: | They propose a semi-supervised approach to annotate unlabeled data from Twitter . they extend FixMatch algorithm to a multimodal setting to account for subjective data . |
| Outcome: | The proposed approach improves on multimodal disaster tweet classification tasks. |
Automated Essay Scoring via Pairwise Contrastive Regression (2022.coling-1)
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| Challenge: | Existing approaches to automate essay scoring use regression or ranking objectives . a novel neural pairwise ranking model is developed to optimize both objectives based on the same loss . |
| Approach: | They propose a novel Neural Pairwise Contrastive Regression model that optimizes both objectives simultaneously as a single loss. |
| Outcome: | The proposed model outperforms previous methods on the public Automated Student Assessment Prize dataset. |
Medical Question Understanding and Answering with Knowledge Grounding and Semantic Self-Supervision (2022.coling-1)
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Khalil Mrini, Harpreet Singh, Franck Dernoncourt, Seunghyun Yoon, Trung Bui, Walter W. Chang, Emilia Farcas, Ndapa Nakashole
| Challenge: | Current medical question answering systems have difficulty processing long, detailed and informally worded questions . a growing number of approaches attempt to enhance the processing of consumer health questions - or medical question understanding . |
| Approach: | They propose a medical question understanding and answering system with knowledge grounding and semantic self-supervision that matches a user question with a trusted medical knowledge base and retrieves a fixed number of relevant sentences from the corresponding answer document. |
| Outcome: | The proposed system retrieves more relevant answers while achieving 20 times faster. |
A Progressive Framework for Role-Aware Rumor Resolution (2022.coling-1)
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| Challenge: | Existing methods for rumor resolution ignore intrinsic propagation mechanisms of rumors and present poor adaptive ability when unprecedented news emerges. |
| Approach: | They propose to identify triggering posts and exploit their characteristics to facilitate rumor verification. |
| Outcome: | The proposed model and scheme exploits rumor diffusion patterns and linguistic features to facilitate verification. |
Uncertainty-aware Propagation Structure Reconstruction for Fake News Detection (2022.coling-1)
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| Challenge: | Existing methods to detect fake news neglect a broader propagation uncertainty issue . Existing studies leverage the user interactions in a social media conversation thread to detect false news. |
| Approach: | They propose a dual graph-based model for improving fake news detection . they propose to explore latent interactions in the actual propagation . |
| Outcome: | The proposed model improves on two real-world datasets showing that it is superior to existing models. |
A Unified Propagation Forest-based Framework for Fake News Detection (2022.coling-1)
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| Challenge: | Recent studies on fake news detection have focused on textual news material, but there is a lack of authoritative regulators. |
| Approach: | They propose a framework to explore latent correlations between propagation trees and a root-induced training strategy to encourage representations of propagation tree to be closer to their prototypical root nodes. |
| Outcome: | The proposed framework explores latent correlations between propagation trees to improve fake news detection. |
CLoSE: Contrastive Learning of Subframe Embeddings for Political Bias Classification of News Media (2022.coling-1)
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| Challenge: | Framing is a political strategy in which journalists and politicians emphasize certain aspects of an issue to influence and sway public opinion. |
| Approach: | They propose a BERT-based model which embeds indicators of frames from news articles in order to predict political bias. |
| Outcome: | The proposed model performs on subframes and political bias classification tasks and is able to detect political bias on both zero-shot and few-shot learning tasks. |
Grammatical Error Correction: Are We There Yet? (2022.coling-1)
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| Challenge: | grammatical error correction (GEC) systems outperform humans on the CoNLL-2014 test set, but there are still classes of errors that they fail to correct. |
| Approach: | They found that state-of-the-art GEC systems outperform humans by a wide margin on the CoNLL-2014 test set . however, they found that there are still classes of errors that they fail to correct . |
| Outcome: | The F0.5 evaluation metric outperforms the CoNLL-2014 test set, but there are still classes of errors that they fail to correct. |
CXR Data Annotation and Classification with Pre-trained Language Models (2022.coling-1)
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Nina Zhou, Ai Ti Aw, Zhuo Han Liu, Cher heng Tan, Yonghan Ting, Wen Xiang Chen, Jordan sim zheng Ting
| Challenge: | Existing tools for clinical data annotation are limited to specific institutions due to differences in writing style, structure, language use and label definition. |
| Approach: | They propose a weak supervision annotation framework with two improvements over existing ones . the framework provides an efficient form of sample selection and data auto-annotation . |
| Outcome: | The proposed framework provides better results for clinical data annotation tasks compared to existing frameworks. |
uChecker: Masked Pretrained Language Models as Unsupervised Chinese Spelling Checkers (2022.coling-1)
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| Challenge: | Chinese Spelling Check (CSC) is a crucial and essential task in the area of natural language processing. |
| Approach: | They propose a framework to conduct unsupervised spelling error detection and correction using masked pretrained language models such as BERT. |
| Outcome: | The proposed model improves character-level and sentence-level accuracy, precision, recall, and F1-Measure on standard datasets. |
Boosting Deep CTR Prediction with a Plug-and-Play Pre-trainer for News Recommendation (2022.coling-1)
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| Challenge: | Personalized news recommendation is a ubiquitous channel in various online applications, such as Google News and MSN News. |
| Approach: | They propose a plug-and-play pre-trainer to learn both user and news encoders through multi-task pre-training. |
| Outcome: | The proposed model improves on existing models and improves inference and updating time. |
Improving Fake News Detection of Influential Domain via Domain- and Instance-Level Transfer (2022.coling-1)
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| Challenge: | Social media spreads both real news and fake news in various domains including politics, health, entertainment, etc. |
| Approach: | They propose a Domain- and Instance-level Transfer Framework for Fake News Detection which could improve the performance of specific target domains. |
| Outcome: | The proposed framework improves performance of target domains by hurting other domains, resulting in unsatisfactory performance in the target domain. |
Student Surpasses Teacher: Imitation Attack for Black-Box NLP APIs (2022.coling-1)
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| Challenge: | Existing MLaaS models are vulnerable to imitation attacks, but none of the stolen models can outperform the original black-box APIs. |
| Approach: | They conduct unsupervised domain adaptation and multi-victim ensemble to show attackers could surpass victims. |
| Outcome: | The proposed model outperforms the original black-box models on transferred domains. |
Combining Compressions for Multiplicative Size Scaling on Natural Language Tasks (2022.coling-1)
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| Challenge: | Quantization, knowledge distillation, and magnitude pruning are among the most popular methods for neural network compression in NLP. |
| Approach: | They compare accuracy vs. model size tradeoffs using quantization and distillation methods . they find that pruning provides greater benefit than quantization . |
| Outcome: | The proposed methods reduce model size and can accelerate inference, but their relative benefit and combinatorial interactions have not been rigorously studied. |
PlugAT: A Plug and Play Module to Defend against Textual Adversarial Attack (2022.coling-1)
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| Challenge: | Recent studies have shown that adversarial examples can be easily fooled by adversarially perturbed examples. |
| Approach: | They propose a pluggable defense module PlugAT to provide robust predictions by adding a few trainable parameters to the model inputs while keeping the original model frozen. |
| Outcome: | The proposed model improves robustness over several strong baselines whilst training only 9.1% parameters. |
Automatic ICD Coding Exploiting Discourse Structure and Reconciled Code Embeddings (2022.coling-1)
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| Challenge: | Existing studies did not exploit the discourse structure of clinical notes, which provides rich contextual information for code assignment. |
| Approach: | They propose to leverage section type classification and section type embeddings to exploit the discourse structure of clinical notes to generate rich contextual information for code assignment. |
| Outcome: | The proposed model outperforms state-of-the-art models on a MIMIC dataset by a large margin. |
Towards Summarizing Healthcare Questions in Low-Resource Setting (2022.coling-1)
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| Challenge: | Existing methods to generate large-scale datasets are difficult in closed domains where human annotation requires domain expertise. |
| Approach: | They propose a method to generate diverse and semantic questions in a low-resource setting with the aim of summarizing healthcare questions. |
| Outcome: | The proposed method generates diverse, fluent, and informative summarized questions on healthcare question summarization datasets. |
Doc-GCN: Heterogeneous Graph Convolutional Networks for Document Layout Analysis (2022.coling-1)
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| Challenge: | Document Layout Analysis tasks rely on visual cues to understand documents . traditional deep learning-based methods fail to recognize the layout and components of unstructured documents based on the document structure and the boundaries of each layout region. |
| Approach: | They propose a way to harmonize and integrate heterogeneous aspects for Document Layout Analysis by using graph convolutional networks to enhance each aspect of features. |
| Outcome: | The proposed task is based on three widely used datasets: PubLayNet, FUNSD, and DocBank. |
Analytic Automated Essay Scoring Based on Deep Neural Networks Integrating Multidimensional Item Response Theory (2022.coling-1)
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| Challenge: | Essay exams have two drawbacks in that grading them is expensive and raises questions about fairness. |
| Approach: | They propose to use a multidimensional item response theory model to improve interpretability while maintaining scoring accuracy. |
| Outcome: | The proposed model improves interpretability while maintaining accuracy while preserving cost and accuracy. |
DP-Rewrite: Towards Reproducibility and Transparency in Differentially Private Text Rewriting (2022.coling-1)
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| Challenge: | Existing systems for differentially private text rewriting lack the means to validate privacy-preserving claims. |
| Approach: | They propose an open-source framework for differentially private text rewriting which is modular, extensible and highly customizable. |
| Outcome: | The proposed framework provides a way to lead and validate private text rewriting research. |
Harnessing Abstractive Summarization for Fact-Checked Claim Detection (2022.coling-1)
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| Challenge: | Social media platforms are becoming battlegrounds for anti-social elements . fact-checking organizations cannot cope with the rapid dissemination of misinformation . a new workflow for fact- checking can be implemented to reduce human time for tasks with high cognition . |
| Approach: | They propose a workflow for detecting previously fact-checked claims that uses abstractive summarization to generate crisp queries. |
| Outcome: | The proposed workflow achieves Recall@5 and MRR of 35% and 0.3, respectively. |
Learning to Generate Explanation from e-Hospital Services for Medical Suggestion (2022.coling-1)
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| Challenge: | Neural models have shown remarkable success in various tasks, however, simply offering the predictions may not satisfy the requirement of end-users. |
| Approach: | They propose a novel model which generates a medical suggestion and provides an explanation as the outline of the reasoning. |
| Outcome: | The proposed model achieves promising performances in both quantitative and human evaluation. |
DeltaNet: Conditional Medical Report Generation for COVID-19 Diagnosis (2022.coling-1)
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Xian Wu, Shuxin Yang, Zhaopeng Qiu, Shen Ge, Yangtian Yan, Xingwang Wu, Yefeng Zheng, S. Kevin Zhou, Li Xiao
| Challenge: | X-ray and CT are the gold standard for COVID-19 diagnosis and treatment . however, due to the excessive number of patients, writing reports becomes a heavy burden for radiologists. |
| Approach: | They propose to use X-ray and CT to generate medical reports automatically . they evaluate DeltaNet on a COVID-19 dataset, where it outperforms state-of-the-art approaches . |
| Outcome: | The proposed system outperforms state-of-the-art methods on a COVID-19 dataset. |
MCS: An In-battle Commentary System for MOBA Games (2022.coling-1)
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Xiaofeng Qi, Chao Li, Zhongping Liang, Jigang Liu, Cheng Zhang, Yuanxin Wei, Lin Yuan, Guang Yang, Lanxiao Huang, Min Li
| Challenge: | In-battle commentary is an important component of live streaming of e-sports competitions and is applicable to a wide range of scenarios like combat information analysis and live streaming. |
| Approach: | They propose a generative system for in-battle real-time commentary in mobile MOBA games and propose 'transform' method to convert match statistics and utterances into consistent encoding space. |
| Outcome: | The proposed system is based on real-time match statistics and events and can be used for live streaming, e-sports commentary and combat information analysis. |
A Two Stage Adaptation Framework for Frame Detection via Prompt Learning (2022.coling-1)
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| Challenge: | Existing frameworks focus on a single scenario or issue, ignoring the special characteristics of frame detection that new events emerge continuously and policy agenda changes dynamically. |
| Approach: | They propose a framework to adapt to different contexts and frame typologies . they propose coding tasks that learn transferable encoders and verbalizers based on pivots and prompts - and generalization tasks that apply them to new issues and label sets. |
| Outcome: | The proposed framework shows superiority in both full-resource and low-resourced conditions. |
Summarizing Patients’ Problems from Hospital Progress Notes Using Pre-trained Sequence-to-Sequence Models (2022.coling-1)
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| Challenge: | Problem list summarization requires a model to understand, abstract, and generate clinical documentation. |
| Approach: | They propose a task that summarises patients' main problems from daily progress notes using input from the provider's progress notes during hospitalization. |
| Outcome: | The proposed model outperforms two state-of-the-art seq2seq transformer architectures in summarizing patients' main problems from daily progress notes in the medical information mart for Intensive Care (MIMIC)-III. |
Human-in-the-loop Robotic Grasping Using BERT Scene Representation (2022.coling-1)
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| Challenge: | Existing approaches for robotic grasping in cluttered scenes are expensive and lack structure information. |
| Approach: | They propose a human-in-the-loop framework for robotic grasping in cluttered scenes . they substitute scene-graph representation with a text representation of the scene using BERT . |
| Outcome: | The proposed framework outperforms object-agnostic and scene-graph based methods on robots and physical robots. |
Automated Chinese Essay Scoring from Multiple Traits (2022.coling-1)
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| Challenge: | Current research on AES focuses on scoring the overall quality or single trait of prompt-specific essays. |
| Approach: | They propose a hierarchical multi-task trait scorer to evaluate quality of writing . they propose an inter-sequence attention mechanism to enhance information interaction . |
| Outcome: | The proposed model outperforms several strong models on ACEA and outperformed other models. |
Semantic-Preserving Adversarial Code Comprehension (2022.coling-1)
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| Challenge: | Existing studies on improving PrLMs for source code comprehension have not found a way to improve both sides of the trade-off between the two aspects. |
| Approach: | They propose to use semantic-preserving code embeddings to find worst-case attacks while forcing the model to predict the correct labels under these worst cases. |
| Outcome: | The proposed model can stay robust against state-of-the-art attacks while boosting the performance of PrLMs for code. |
Continually Detection, Rapidly React: Unseen Rumors Detection Based on Continual Prompt-Tuning (2022.coling-1)
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| Challenge: | Existing rumor detection models assume the same training and testing distributions and can not cope with the continuously changing social network environment. |
| Approach: | They propose a Continual Prompt-Tuning RD framework which avoids catastrophic forgetting of upstream tasks during sequential task learning and enables bidirectional knowledge transfer between domain tasks. |
| Outcome: | The proposed framework avoids catastrophic forgetting (CF) of upstream tasks during sequential task learning and enables bidirectional knowledge transfer between domain tasks. |
AiM: Taking Answers in Mind to Correct Chinese Cloze Tests in Educational Applications (2022.coling-1)
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| Challenge: | Existing methods to correct handwritten assignments are to use OCR to recognize characters and compare them to answers. |
| Approach: | They propose a multimodal approach to correct handwritten Chinese characters by combining the visual information of students' handwriting with the encoded representations of answers. |
| Outcome: | The proposed model outperforms OCR-based methods by a large margin. |
TreeMAN: Tree-enhanced Multimodal Attention Network for ICD Coding (2022.coling-1)
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| Challenge: | Existing methods to automatically assign ICD codes ignore crucial information contained in structured medical data, which is hard to be captured from the noisy clinical notes. |
| Approach: | They propose to use a Tree-enhanced multimodal attention network to fuse tabular features and textual features into multimodal representations by enhancing the text representations with tree-based features. |
| Outcome: | The proposed method outperforms state-of-the-art methods on two MIMIC datasets. |
Gated Mechanism Enhanced Multi-Task Learning for Dialog Routing (2022.coling-1)
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| Challenge: | Existing methods for dialog routing are mostly heuristic and cannot achieve high-quality performance. |
| Approach: | They propose a multi-task learning framework with a dialog encoder and two tailored gated mechanism modules to solve this problem. |
| Outcome: | The proposed model can play the role of hierarchical information filtering and is non-invasive to existing dialog systems. |
Negation, Coordination, and Quantifiers in Contextualized Language Models (2022.coling-1)
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| Challenge: | Recent work has focused on specific tasks and on the learning outcome. |
| Approach: | They propose to decouple the weaknesses from specific tasks and focus on the embeddings per se and their mode of learning. |
| Outcome: | The proposed model can learn semantic constraints and how the context impacts their embeddings. |
Tales and Tropes: Gender Roles from Word Embeddings in a Century of Children’s Books (2022.coling-1)
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Anjali Adukia, Patricia Chiril, Callista Christ, Anjali Das, Alex Eble, Emileigh Harrison, Hakizumwami Birali Runesha
| Challenge: | In 100 years of influential children's books, gender is portrayed in a way that reproduces traditional gender norms in society. |
| Approach: | They use word embeddings to train a model to detect individual sentences containing stereotypes to measure how gender is portrayed in children's books. |
| Outcome: | The proposed model trains a model to detect individual sentences containing stereotypes to gain a deeper understanding of the messages conveyed to children by the books they read. |
CLOWER: A Pre-trained Language Model with Contrastive Learning over Word and Character Representations (2022.coling-1)
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Borun Chen, Hongyin Tang, Jiahao Bu, Kai Zhang, Jingang Wang, Qifan Wang, Hai-Tao Zheng, Wei Wu, Liqian Yu
| Challenge: | Pre-trained language models (PLMs) have achieved remarkable performance gains across numerous downstream tasks in natural language understanding. |
| Approach: | They propose a Chinese pre-trained language model that implicitly encodes words into characters . they propose 'contrastive learning over word' and 'character' representations to improve learning . |
| Outcome: | The proposed model can encode words into fine-grained representations without modification of production pipelines. |
On the Nature of BERT: Correlating Fine-Tuning and Linguistic Competence (2022.coling-1)
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| Challenge: | Several studies on the interpretation of Neural Language Models (NLMs) focus on the linguistic generalization abilities of pre-trained models, but little attention is paid to how the linguistic knowledge of the models changes during fine-tuning. |
| Approach: | They propose to examine whether a wide range of linguistic phenomena are forgotten during fine-tuning and whether it is possible to predict the fine- tuned accuracy solely relying on the assessed linguistic competence. |
| Outcome: | The proposed model can predict the evolution of written language competence of native language learners based on the assessed linguistic competence. |
LayerConnect: Hypernetwork-Assisted Inter-Layer Connector to Enhance Parameter Efficiency (2022.coling-1)
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| Challenge: | Existing parameter-efficient methods focus on reducing trainable parameters but neglect the inference speed, which limits the ability to deploy PLMs. |
| Approach: | They propose to use a hypernetwork-assisted inter-layer connector to enhance inference efficiency by tuning parameters inside a linear connector between two Transformer layers. |
| Outcome: | The proposed model reduces model parameters to 11.75% while preserving performance degradation to less than 5%. |
Effect of Post-processing on Contextualized Word Representations (2022.coling-1)
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| Challenge: | Post-processing of static embeddings has been shown to improve their performance on both lexical and sequence-level tasks. |
| Approach: | They standardize individual neuron activations using z-score, min-max normalization, and remove top principal components using the all-but-the-top method. |
| Outcome: | The proposed method unwraps vital information present in the representations for both lexical and sequence classification tasks. |
Does BERT Rediscover a Classical NLP Pipeline? (2022.coling-1)
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| Challenge: | Existing theories of BERT's structure lack conclusive empirical support . however, there is scepticism about the premises of probing itself . |
| Approach: | They propose a new probe called GridLoc that can take into account token positions, training rounds, and random seeds. |
| Outcome: | The proposed probe detects other, stronger regularities suggesting appeals to layer depth may not be the preferable mode of explanation for BERT’s inner workings. |
HG2Vec: Improved Word Embeddings from Dictionary and Thesaurus Based Heterogeneous Graph (2022.coling-1)
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| Challenge: | Existing models that learn word embeddings rely on a large corpus of data . however, these models require massive time and space for data pre-processing and training . |
| Approach: | They propose a model that learns word embeddings utilizing only dictionaries and thesauri . they exploit a new context-focused loss model that models transitive relationships between word pairs . |
| Outcome: | The proposed model reaches the state-of-art on multiple word similarity and relatedness benchmarks. |
Transferring Knowledge from Structure-aware Self-attention Language Model to Sequence-to-Sequence Semantic Parsing (2022.coling-1)
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| Challenge: | Semantic parsing aims to map a natural language sentence into a machine executable formal representation. |
| Approach: | They propose a structure-aware self-attention language model to capture structural information of target representations and propose incorporating it into a seq2seq model. |
| Outcome: | The proposed model improves the baseline model on four semantic parsing and Python code generation tasks. |
Enhancing Contextual Word Representations Using Embedding of Neighboring Entities in Knowledge Graphs (2022.coling-1)
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| Challenge: | Existing methods for pre-trained language models lack explicit grounding in real-world entities. |
| Approach: | They propose a mechanism that integrates the structure of a KG into recent PLM architectures by generalizing the embeddings of neighboring entities. |
| Outcome: | The proposed method improves a classification task, entity typing task and language comprehension tasks. |
Generic Overgeneralization in Pre-trained Language Models (2022.coling-1)
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| Challenge: | Generic statements such as "ducks lay eggs" are perceived as false universally . however, universally quantified statements such "all tigers have stripes" should be perceived as true . |
| Approach: | They investigate the generic overgeneralization effect in pre-trained language models . they show that pre-trainers tend to treat quantified generic statements as if they were true . |
| Outcome: | The proposed model reduces, but does not eliminate, generic overgeneralization bias . the model can be used to inject factual knowledge about kinds into pre-trained models . |
How about Time? Probing a Multilingual Language Model for Temporal Relations (2022.coling-1)
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| Challenge: | XLM-R is a multilingual language model for temporal relation classification between events in four languages. |
| Approach: | They propose to use a multilingual language model for temporal relation classification between events in four languages to obtain contextualized embeddings. |
| Outcome: | The proposed model outperforms state-of-the-art models in obtaining competitive results against state- of-the art systems, but lacks suitable encoded information to address this task. |
CogBERT: Cognition-Guided Pre-trained Language Models (2022.coling-1)
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| Challenge: | Existing methods fine-tune pre-trained models on cognitive data, ignoring the semantic gap between texts and cognitive signals. |
| Approach: | They propose a framework that can induce fine-grained cognitive features from cognitive data and incorporate them into pre-trained language models by adaptively adjusting the weight of cognitive features for different NLP tasks. |
| Outcome: | The proposed framework can induce fine-grained cognitive features from cognitive data and incorporate them into BERT by adaptively adjusting weight of cognitive features for different NLP tasks. |
Can Transformers Process Recursive Nested Constructions, Like Humans? (2022.coling-1)
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| Challenge: | A recent study evaluated recursive processing in recurrent neural language models (RNN-LMs) and showed that such models perform below chance level on embedded dependencies within nested constructions. |
| Approach: | They evaluated recursive processing in recurrent neural language models and found that Transformers perform below chance level on embedded dependencies within nested constructions. |
| Outcome: | The proposed models perform below chance level on embedded dependencies within nested constructions, compared to humans. |
NSP-BERT: A Prompt-based Few-Shot Learner through an Original Pre-training Task —— Next Sentence Prediction (2022.coling-1)
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| Challenge: | Recent studies have shown that using prompts to utilize language models to perform downstream tasks is more effective than using token-level methods such as PET. |
| Approach: | They propose to use a BERT original pre-training task abandoned by RoBERTa and other models to construct a sentence-level prompt-based method that does not need to fix the length of the prompt or the position to be predicted. |
| Outcome: | The proposed method performs better than PET and EFL on a BERT pre-training task and is comparable to other prompt-based methods. |
MetaPrompting: Learning to Learn Better Prompts (2022.coling-1)
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| Challenge: | Recent research on prompting moves from discrete tokens based "hard prompts" to continuous "soft prompts", which employ learnable vectors as pseudo prompt tokens and achieve better performance. |
| Approach: | They propose a generalized soft prompting method that uses model-agnostic meta-learning to find better initialization for soft prompts. |
| Outcome: | The proposed method improves on three datasets and brings new state-of-the-art performance. |
Parameter-Efficient Mixture-of-Experts Architecture for Pre-trained Language Models (2022.coling-1)
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| Challenge: | Recent results show that the mix-of-experts architecture is parameter inefficient . large-scale pre-trained language models can achieve excellent performance in many NLP tasks. |
| Approach: | They propose to build a parameter-efficient mix-of-experts architecture by sharing information across experts. |
| Outcome: | The proposed architecture increases model capacity without increasing computation costs. |
Pre-trained Token-replaced Detection Model as Few-shot Learner (2022.coling-1)
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| Challenge: | Pre-trained masked language models have demonstrated remarkable few-shot learning ability . a novel approach to few- shot learning with pre-tried token-replaced detection models is proposed . |
| Approach: | They propose a method to reformulate a classification or regression task as a token-replaced detection problem by using pre-trained token-based models. |
| Outcome: | The proposed approach outperforms pre-trained masked language models in learning tasks . it can learn models with a few examples and generalize well from limited examples like humans . |
Evaluating Diversity of Multiword Expressions in Annotated Text (2022.coling-1)
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| Challenge: | Using the extensive formalization and measures of diversity developed in ecology, we evaluate the variety and balance of multiword expression annotation produced by automatic annotation systems. |
| Approach: | They propose to use the formalization and measures of diversity developed in ecology to evaluate the variety and balance of multiword expression annotation produced by automatic annotation systems. |
| Outcome: | The proposed measures validate or invalidate their pertinence for multiword expressions in annotated texts. |
CausalQA: A Benchmark for Causal Question Answering (2022.coling-1)
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Alexander Bondarenko, Magdalena Wolska, Stefan Heindorf, Lukas Blübaum, Axel-Cyrille Ngonga Ngomo, Benno Stein, Pavel Braslavski, Matthias Hagen, Martin Potthast
| Challenge: | Existing causal question answering datasets are relatively small and only include one type of causal question. |
| Approach: | They construct a benchmark corpus of 1.1 million causal questions with answers . they use a typology derived from a data-driven, manual analysis of QA datasets . |
| Outcome: | The proposed model achieves a ROUGE-L F1 score of 0.48 on the new QA benchmark. |
MACRONYM: A Large-Scale Dataset for Multilingual and Multi-Domain Acronym Extraction (2022.coling-1)
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Amir Pouran Ben Veyseh, Nicole Meister, Seunghyun Yoon, Rajiv Jain, Franck Dernoncourt, Thien Huu Nguyen
| Challenge: | Acronym extraction is the task of identifying acronyms and their expanded forms in texts . existing AE methods for English are limited to specific languages and domains . |
| Approach: | They propose to annotate 27,200 sentences in 6 different languages and 2 new domains for AE. |
| Outcome: | The proposed dataset shows that AE in different languages and learning settings has unique challenges . |
Curating a Large-Scale Motivational Interviewing Dataset Using Peer Support Forums (2022.coling-1)
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| Challenge: | Existing therapeutic chatbots lack large-scale conversations between clients and trained counselors . prior work has found that social media platforms such as Reddit are used to vent distress and peers are seen to actively respond to such posts. |
| Approach: | They propose to use peer support platforms to scrape conversational data from Reddit to determine whether counselors' responses align with real therapeutic conversations. |
| Outcome: | The proposed method achieved 97% coverage out of 17.3K responses, meaning that out of 16.8K responses labeled with a moderate agreement. |
CCTC: A Cross-Sentence Chinese Text Correction Dataset for Native Speakers (2022.coling-1)
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| Challenge: | Chinese text correction datasets focus on detecting and correcting Chinese spelling errors and grammatical errors. |
| Approach: | They propose a Chinese text correction dataset for native speakers . they manually annotated 1,500 Chinese texts written by native speakers. |
| Outcome: | The proposed dataset can detect and correct Chinese spelling errors and grammatical errors. |
RealMedDial: A Real Telemedical Dialogue Dataset Collected from Online Chinese Short-Video Clips (2022.coling-1)
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| Challenge: | Existing medical dialogue systems are limited by the lack of corpora and data from real scenarios. |
| Approach: | They construct a Chinese medical dialogue dataset based on real medical consultations. |
| Outcome: | The proposed dataset is applicable to a wide range of NLP tasks with respect to medical dialogue. |
TempoWiC: An Evaluation Benchmark for Detecting Meaning Shift in Social Media (2022.coling-1)
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Daniel Loureiro, Aminette D’Souza, Areej Nasser Muhajab, Isabella A. White, Gabriel Wong, Luis Espinosa-Anke, Leonardo Neves, Francesco Barbieri, Jose Camacho-Collados
| Challenge: | Language models are often clean and time-invariant, and do little to no account of social media usage. |
| Approach: | They propose a benchmark to accelerate research in social media-based meaning shift. |
| Outcome: | The proposed benchmark is aimed at accelerating research in social media-based meaning shift. |
Automatic Generation of Large-scale Multi-turn Dialogues from Reddit (2022.coling-1)
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| Challenge: | Using a set of algorithms, we can generate large dialogue corpus from Reddit. |
| Approach: | They propose to automatically convert posts and their comments from discussion forums such as Reddit into multi-turn dialogues. |
| Outcome: | The proposed methods improve on the baseline method by 36.3% . the best method shows an improvement of 36.6% over the previous one . |
ConFiguRe: Exploring Discourse-level Chinese Figures of Speech (2022.coling-1)
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| Challenge: | Figures of speech often deviate from their literal meanings to express deeper semantic implications. |
| Approach: | They propose a concept of figurative unit, which is the carrier of a figure, and build a Chinese corpus for Contextualized Figure Recognition. |
| Outcome: | The proposed model is based on 12 types of figures commonly used in Chinese . it shows that the proposed tasks are challenging for existing models . |
Twitter Topic Classification (2022.coling-1)
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Dimosthenis Antypas, Asahi Ushio, Jose Camacho-Collados, Vitor Silva, Leonardo Neves, Francesco Barbieri
| Challenge: | Existing methods to identify topics from posts are difficult to interpret and can differ from corpus to corpus. |
| Approach: | They propose a task based on tweet topic classification and release two datasets that can be used to train and test models. |
| Outcome: | The proposed task is based on two datasets from recent time periods and provides training and testing data. |
Layer or Representation Space: What Makes BERT-based Evaluation Metrics Robust? (2022.coling-1)
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| Challenge: | Recent embedding-based evaluation metrics for text generation are based on measuring correlation with human evaluations on standard benchmarks. |
| Approach: | They examine the robustness of BERTScore, one of the most popular embedding-based metrics for text generation. |
| Outcome: | The embedding-based metrics that have the highest correlation with human evaluations on a standard benchmark can have the lowest correlation if the amount of input noise or unknown tokens increases. |
Evaluating the Performance of Transformer-based Language Models for Neuroatypical Language (2022.coling-1)
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| Challenge: | Difficulties with social aspects of language are among the hallmarks of autism spectrum disorder (ASD). |
| Approach: | They propose a transformer-based framework for identifying linguistic features associated with social aspects of communication using a corpus of conversations between adults with and without ASD and neurotypical conversational partners. |
| Outcome: | The proposed framework yields strong accuracy overall, but performance is significantly worse for the language of participants with ASD, suggesting they use a more diverse set of strategies for some social linguistic functions. |
TERMinator: A System for Scientific Texts Processing (2022.coling-1)
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| Challenge: | Existing datasets with annotations of scientific terms and relations are difficult to find for other fields, such as biomedical and multi-domains. |
| Approach: | They present a dataset that includes annotations for two tasks and develop a system called TERMinator for the study of the influence of language models on term recognition. |
| Outcome: | The proposed system improves the quality of the extracted entities and relations in Russian. |
LipKey: A Large-Scale News Dataset for Absent Keyphrases Generation and Abstractive Summarization (2022.coling-1)
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| Challenge: | Existing work has addressed each element individually, but this study focuses on LipKey, the largest news corpus with human-written abstractive summaries, absent keyphrases, and titles. |
| Approach: | They propose a novel news dataset that consists of highly absent keyphrases . they combine lips keyphrase and TF-IDF to obtain abstractive summaries . |
| Outcome: | The proposed dataset is the largest news corpus with human-written abstractive summaries, absent keyphrases, and titles. |
Understanding Attention for Vision-and-Language Tasks (2022.coling-1)
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| Challenge: | Attention mechanism has been used in Vision-and-Language (VL) tasks to bridge the semantic gap between visual and textual clues. |
| Approach: | They conduct a comprehensive analysis on understanding the role of attention alignment by looking into attention score calculation methods and checking how it represents the visual region’s and textual token’s significance for the global assessment. |
| Outcome: | The attention score calculation methods represent visual region’s and textual token’s significance for the global assessment. |
Effective Data Augmentation for Sentence Classification Using One VAE per Class (2022.coling-1)
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| Challenge: | Variational auto-encoders and its conditional variant the Conditional-VAE (CVAE) are often used to generate new textual data, but they require more complex manipulations to ensure that the generated examples are useful. |
| Approach: | They propose a simple way to use Variational Auto-Encoders (VAE) for data augmentation by training one VAE per class. |
| Outcome: | The proposed method outperforms generative models on binary classification tasks and several dataset sizes on four different tasks. |
NLG-Metricverse: An End-to-End Library for Evaluating Natural Language Generation (2022.coling-1)
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| Challenge: | Natural language generation models are a key component of deep learning, says aaron eliott . he says it is crucial to develop and apply better metrics for NLG evaluation . |
| Approach: | a new open-source library for NLG evaluation is created to facilitate researchers to judge the effectiveness of their models. the framework provides a living collection of NLG metrics in a unified and easy-to-use environment. |
| Outcome: | a new open-source library for NLG evaluation aims to improve performance of models . the framework provides tools to apply, analyze, compare, and visualize the metrics . |
TestAug: A Framework for Augmenting Capability-based NLP Tests (2022.coling-1)
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| Challenge: | Existing work on capability-based testing requires the developer to compose each individual test template from scratch. |
| Approach: | They propose a capability-based NLP testing framework that requires the developer to only annotate a few test templates while leveraging the GPT-3 engine to generate the majority of test cases. |
| Outcome: | The proposed framework saves the developer's manual efforts and guarantees the correctness of the generated suites with a validity checker. |
KoCHET: A Korean Cultural Heritage Corpus for Entity-related Tasks (2022.coling-1)
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| Challenge: | Existing corpus for entity-related tasks is limited in terms of application and cannot be used for entity recognition. |
| Approach: | They propose to use a Korean cultural heritage corpus for the typical entity-related tasks named entity recognition (NER), relation extraction (RE) and entity typing (ET) . |
| Outcome: | The proposed corpus makes it more useful in terms of cultural heritage and provides practical insights in terms linguistic analysis. |
MonoByte: A Pool of Monolingual Byte-level Language Models (2022.coling-1)
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| Challenge: | Existing studies have shown that multilingual models can achieve zero-shot cross-lingual performance on various NLP tasks, but due to the cost of pretraining, they often use public models with limited budgets. |
| Approach: | They propose to use tokenized models to test cross-lingual ability in multilingual and monolingual corpora. |
| Outcome: | The results show that models pretrained on multilingual and even monolingual corpora perform better than models pre-trained on SOTA models. |
Wizard of Tasks: A Novel Conversational Dataset for Solving Real-World Tasks in Conversational Settings (2022.coling-1)
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Jason Ingyu Choi, Saar Kuzi, Nikhita Vedula, Jie Zhao, Giuseppe Castellucci, Marcus Collins, Shervin Malmasi, Oleg Rokhlenko, Eugene Agichtein
| Challenge: | Existing Conversational Task Assistants fail to provide a comprehensive natural conversation that includes search, context-aware QA, step-by-step instructions. |
| Approach: | They present a corpus of conversations in two domains: cooking and home improvement . they crowd-sourced 549 conversations with an asynchronous Wizard-of-Oz setup . |
| Outcome: | The proposed model performs well in both Intent Classification and Abstractive Question Answering tasks, but the performance is poor on AQA tasks. |
K-MHaS: A Multi-label Hate Speech Detection Dataset in Korean Online News Comment (2022.coling-1)
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| Challenge: | Online hate speech detection resources in other languages are limited. |
| Approach: | They introduce a new dataset for hate speech detection that handles Korean language patterns. |
| Outcome: | The proposed dataset outperforms existing datasets in Korean language classifications. |
Domain- and Task-Adaptation for VaccinChatNL, a Dutch COVID-19 FAQ Answering Corpus and Classification Model (2022.coling-1)
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| Challenge: | VaccinChatNL is the first FAQ chatbot with over 12k user queries . it can be used to find the representative question that matches a user's request . |
| Approach: | They build a FAQ chatbot from 50 question-answer pairs and annotate user questions with appropriate or new answer classes. |
| Outcome: | The VaccinChatNL is the first publicly available Dutch FAQ answering corpus with large groups of human-paraphrased questions. |
Benchmarking Automated Clinical Language Simplification: Dataset, Algorithm, and Evaluation (2022.coling-1)
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| Challenge: | Existing studies to translate medical jargon into layperson-understandable language focus on accuracy and readability aspects of clinical language. |
| Approach: | They propose to construct a dataset to support automated clinical language simplification and propose a model that mimics the human annotation procedure. |
| Outcome: | The proposed model matches human annotation procedures and achieves state-of-the-art performance compared with baselines. |
WikiHan: A New Comparative Dataset for Chinese Languages (2022.coling-1)
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| Challenge: | Currently, there are 1.3 billion speakers of Sinitic varieties, making the family one of the largest in terms of speaker count. |
| Approach: | They have collected a single constituent and structured form of Chinese varieties for comparative linguistics and Chinese NLP. |
| Outcome: | The proposed dataset contains 67,943 entries across 8 varieties and Middle Chinese . it achieves 54.11% accuracy and 17.69% error rate on a protoform reconstruction task . |
Visual Recipe Flow: A Dataset for Learning Visual State Changes of Objects with Recipe Flows (2022.coling-1)
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Keisuke Shirai, Atsushi Hashimoto, Taichi Nishimura, Hirotaka Kameko, Shuhei Kurita, Yoshitaka Ushiku, Shinsuke Mori
| Challenge: | a new dataset enables us to learn a cooking action result for each object in a recipe text. |
| Approach: | They propose a multimodal dataset that enables us to learn a cooking action result for each object in a recipe text. |
| Outcome: | The proposed dataset reduces human annotation costs by allowing multimodal information retrieval. |
IMPARA: Impact-Based Metric for GEC Using Parallel Data (2022.coling-1)
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| Challenge: | Existing methods for automatic evaluation of grammatical error correction require multiple reference sentences or manual scores. |
| Approach: | They propose an Impact-based Metric for GEC using PARAllel data, IMPARA . IMPRA computes correction impacts computed by parallel data comprising pairs of grammatical/ungrammatically-spaced sentences. |
| Outcome: | The proposed method can perform evaluations that fit different domains and correction styles. |
Evons: A Dataset for Fake and Real News Virality Analysis and Prediction (2022.coling-1)
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| Challenge: | Existing collections of fake news articles contain claims fact-checked for veracity . Developing fake news detection models requires annotated collections of real and fake news. |
| Approach: | They propose to annotate news articles originating from fake and real news media sources for the analysis and prediction of news virality. |
| Outcome: | The proposed collection is compared with existing datasets which contain claims or headline and body text but can't be used for predicting fake news virality. |
Are Pretrained Multilingual Models Equally Fair across Languages? (2022.coling-1)
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| Challenge: | Pretrained multilingual language models can help bridge the digital language divide, enabling high-quality NLP models for lower-resourced languages. |
| Approach: | They propose to use a multilingual dataset to examine whether multilingual models are equally fair across languages. |
| Outcome: | The proposed model enables apples-to-apples comparison across languages of group disparities in multilingual language models. |
Possible Stories: Evaluating Situated Commonsense Reasoning under Multiple Possible Scenarios (2022.coling-1)
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| Challenge: | Current studies in natural language processing do not focus on situated commonsense reasoning under multiple possible scenarios. |
| Approach: | They frame a scenario task by asking multiple questions with the same set of possible endings as candidate answers, given a short story text. |
| Outcome: | The proposed dataset shows that even strong pretrained models struggle to answer the questions consistently, highlighting that the highest accuracy in an unsupervised setting (60.2%) is far behind human accuracy (92.5%). |
DiaBiz.Kom - towards a Polish Dialogue Act Corpus Based on ISO 24617-2 Standard (2022.coling-1)
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Marcin Oleksy, Jan Wieczorek, Dorota Drużyłowska, Julia Klyus, Aleksandra Domogała, Krzysztof Hwaszcz, Hanna Kędzierska, Daria Mikoś, Anita Wróż
| Challenge: | DiaBiz.Kom is the first corpus of dialogue texts in the Polish language . it contains transcriptions of telephone conversations conducted according to a prepared scenario. |
| Approach: | They describe the specification and evaluation of DiaBiz.Kom - the corpus of dialogue texts in Polish. |
| Outcome: | The proposed corpus contains transcriptions of telephone conversations conducted according to a prepared scenario and will be used to develop a system of dialog analysis and modules for creating advanced chatbots. |
Towards Explainable Evaluation of Language Models on the Semantic Similarity of Visual Concepts (2022.coling-1)
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Maria Lymperaiou, George Manoliadis, Orfeas Menis Mastromichalakis, Edmund G. Dervakos, Giorgos Stamou
| Challenge: | Recent advances in NLP research have focused on robustness and explainability issues of their evaluation strategies. |
| Approach: | They propose to use pre-trained transformers to evaluate semantic similarity for visual vocabularies . they propose to provide explainable metrics for understanding the quality of retrieved instances . |
| Outcome: | The proposed metrics highlight inabilities of widely used evaluation methods and highlight weaknesses in learned linguistic representations. |
Establishing Annotation Quality in Multi-label Annotations (2022.coling-1)
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| Challenge: | Multi-label annotations allow multiple interpretations of a single item, but they also affect the chance that two coders agree with each other. |
| Approach: | They propose a bootstrapped method to obtain chance agreement for each measure and a method to get an adjusted agreement coefficient that is more interpretable. |
| Outcome: | The proposed method allows for an adjusted agreement coefficient that is more interpretable on simulated datasets. |
Biographically Relevant Tweets – a New Dataset, Linguistic Analysis and Classification Experiments (2022.coling-1)
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| Challenge: | Unlike previous work, we do not restrict biographical relevance to a small fixed set of pre-defined relations. |
| Approach: | They propose a dataset comprising tweets for the novel task of detecting biographically relevant utterances. |
| Outcome: | The proposed dataset focuses on biographical information on ordinary users of Twitter. |
BECEL: Benchmark for Consistency Evaluation of Language Models (2022.coling-1)
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| Challenge: | Existing definitions of behavioural consistency are inconsistent across many studies. |
| Approach: | They propose a behavioural consistency model and propose behavioural taxonomy that classifies consistencies into several sub-categories. |
| Outcome: | The proposed model performs poorly on 19 test cases while exhibiting high inconsistency in many cases. |
KoBEST: Korean Balanced Evaluation of Significant Tasks (2022.coling-1)
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| Challenge: | a well-formulated benchmark allows objective and precise evaluation of diverse models. |
| Approach: | They propose a benchmark for Korean balanced evaluation of significant tasks that requires advanced Korean linguistic knowledge. |
| Outcome: | The proposed benchmarks are based on five Korean-language downstream tasks . the data is annotated by humans and thoroughly reviewed to guarantee high data quality. |
A New Public Corpus for Clinical Section Identification: MedSecId (2022.coling-1)
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| Challenge: | a study aims to segment sections of clinical medical domain documentation . section identification is a process by which sections are demarcated and labeled . |
| Approach: | They use a set of 2,002 fully annotated medical notes from the MIMIC-III to segment sections in clinical medical domain documentation. |
| Outcome: | The proposed model shows that medical concepts are related across sections using principal component analysis. |
A Data-driven Approach to Named Entity Recognition for Early Modern French (2022.coling-1)
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| Challenge: | Named entity recognition is an important task in natural language processing. |
| Approach: | They propose to use a data-driven approach to identify historical French with fine-grained annotations instead of a specialised architecture to tackle particularities. |
| Outcome: | The proposed corpus is larger than the most popular NER evaluation corpora for both Contemporary English and French. |
Reproducibility and Automation of the Appraisal Taxonomy (2022.coling-1)
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| Challenge: | Existing methods for Appraisal annotation are descriptive and lack of data hinders progress . |
| Approach: | They propose to use annotated data to measure the performance of automated Appraisal annotations in a publicly available dataset. |
| Outcome: | The proposed methods show poor agreement at more detailed categories and fair agreement at coarse-level categories. |
Few-Shot Table Understanding: A Benchmark Dataset and Pre-Training Baseline (2022.coling-1)
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| Challenge: | Pre-trained language models have demonstrated their effectiveness for few-shot table understanding, but few-shoot table understanding is rarely explored due to the deficiency of public table pre-training corpus and well-defined downstream benchmark tasks. |
| Approach: | They establish a benchmark dataset and use it to explore few-shot table understanding in Chinese. |
| Outcome: | The proposed model improves the few-shot table understanding in Chinese. |
Tafsir Dataset: A Novel Multi-Task Benchmark for Named Entity Recognition and Topic Modeling in Classical Arabic Literature (2022.coling-1)
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Sajawel Ahmed, Rob van der Goot, Misbahur Rehman, Carl Kruse, Ömer Özsoy, Alexander Mehler, Gemma Roig
| Challenge: | Named entity recognition and topic modeling are crucial for downstream tasks in natural language processing. |
| Approach: | They propose to address named entity recognition and topic modeling on CA literature . they manually annotate the work of Tafsir Al-Tabari with span-based NEs . |
| Outcome: | The results show that the proposed task can perform state-of-the-art on historical topic models. |
Resource of Wikipedias in 31 Languages Categorized into Fine-Grained Named Entities (2022.coling-1)
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| Challenge: | a resource of Wikipedias in 31 languages is categorized into Extended Named Entity (ENE) ENE version 8 has 219 fine-grained NE categories. |
| Approach: | They describe a resource of Wikipedias in 31 languages categorized into Extended Named Entity (ENE) they first categorized 920 K Japanese Wikipedia pages using machine learning, then shared a task of Wikipedia categorization into 30 languages . |
| Outcome: | The proposed system is based on a dataset of Japanese Wikipedia pages . the dataset shows the best performance among the 30 languages . |
Accuracy meets Diversity in a News Recommender System (2022.coling-1)
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| Challenge: | Existing news recommender systems use news stories that users have read in the past to infer their interests and preferences. |
| Approach: | They propose a two-tower architecture that learns news representation through a news item tower and users’ representations through s query towers. |
| Outcome: | The proposed architecture achieves a balance between accuracy and diversity on two news datasets. |
Dynamic Nonlinear Mixup with Distance-based Sample Selection (2022.coling-1)
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| Challenge: | Existing methods to augment data with mixup are limited by the space of synthetic data and its regularization effect. |
| Approach: | They propose a dynamic nonlinear mixup with distance-based sample selection which generates multiple sample pairs based on the distance between each sample. |
| Outcome: | The proposed method outperforms state-of-the-art methods on multiple public datasets. |
MultiCoNER: A Large-scale Multilingual Dataset for Complex Named Entity Recognition (2022.coling-1)
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| Challenge: | Named Entity Recognition (NER) is a core task in Natural Language Processing. |
| Approach: | They present a large multilingual dataset for Named Entity Recognition that covers 3 domains across 11 languages and multilingual and code-mixing subsets. |
| Outcome: | The proposed dataset is large and multilingual, covering 11 languages and subsets. |
Extracting a Knowledge Base of COVID-19 Events from Social Media (2022.coling-1)
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| Challenge: | a flood of COVID-19 related information has appeared on social media since December 2019 . this includes reports on public figures who have tested positive/negative for the virus . |
| Approach: | They construct a corpus of 10,000 tweets with annotated public reports of five COVID-19 events, using slot-filling questions to fill in slots. |
| Outcome: | The proposed method can be quickly applied to develop knowledge bases for new domains in response to emerging crises, including natural disasters or future disease outbreaks. |
Accounting for Language Effect in the Evaluation of Cross-lingual AMR Parsers (2022.coling-1)
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| Challenge: | Existing multilingual AMR evaluation metrics are not available for cross-lingual parsers . existing studies show that source language has a dramatic effect on cross-linguistic AMRs . |
| Approach: | They propose to use three multilingual adaptations of monolingual AMR evaluation metrics to evaluate cross-lingual AML parsers. |
| Outcome: | The proposed metric is the most highly correlated to english AMRs, while the most correlated is S2match. |
QSTS: A Question-Sensitive Text Similarity Measure for Question Generation (2022.coling-1)
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| Challenge: | Existing measures for question generation have been inadequately evaluated . current research uses QA datasets containing pairs of (reference question, passage context) elements. |
| Approach: | They propose a Question-Sensitive Text Similarity measure for comparing two questions . they also propose enabling question similarity research in QG contexts by using a dataset called SimQG. |
| Outcome: | The proposed measure overcomes shortcomings of existing measures that depend on n-gram overlap scores and obtains superior results compared to existing measures on publicly-available QG datasets. |
Noun-MWP: Math Word Problems Meet Noun Answers (2022.coling-1)
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| Challenge: | Existing MWP solvers can handle Noun-MWPs, but they are not as efficient as other models. |
| Approach: | They propose a method to empower existing MWP solvers to handle Noun-MWPs. |
| Outcome: | The proposed model solves Noun-MWPs significantly better than other models and solves conventional MWP problems as well. |
ViNLI: A Vietnamese Corpus for Studies on Open-Domain Natural Language Inference (2022.coling-1)
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| Challenge: | a large-scale corpus is needed for studies on natural language inference (NLI) for Vietnamese, which can be considered a low-resource language. |
| Approach: | They propose a corpus for evaluating Vietnamese natural language inference models . they use a human-annotated corpus extracted from more than 800 online news articles . |
| Outcome: | The ViNLI corpus is created and evaluated with a strict process of quality control . the best system performance is still far from human performance (a 14.20% gap in accuracy). |
InferES : A Natural Language Inference Corpus for Spanish Featuring Negation-Based Contrastive and Adversarial Examples (2022.coling-1)
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| Challenge: | InferES is an original corpus for Natural Language Inference (NLI) in European Spanish . |
| Approach: | They propose to implement and analyze a corpus-creating strategy utilizing expert linguists and crowd workers to provide high-quality data and facilitate the systematic evaluation of automated systems. |
| Outcome: | The proposed model obtains 72.8% accuracy and performs moderately well on negation-based adversarial examples. |
ParaZh-22M: A Large-Scale Chinese Parabank via Machine Translation (2022.coling-1)
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| Challenge: | Paraphrasing is an important data augmentation approach for natural language processing (NLP). |
| Approach: | They propose to extract sentence-level paraphrases from multiple Chinese translations and construct a larger Chinese parabank with 22M sentence pairs. |
| Outcome: | The proposed parabank is the largest to date in Chinese, but limited by one-to-many translation data. |
ESimCSE: Enhanced Sample Building Method for Contrastive Learning of Unsupervised Sentence Embedding (2022.coling-1)
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| Challenge: | a new method for learning unsupervised sentence embeddings is proposed . unsup-SimCSE is biased because of the length information encoded into the sentence embeds . |
| Approach: | They propose a new unsupervised sentence embedding method that uses dropout to obtain positive pairs from a pre-trained Transformer encoder. |
| Outcome: | The proposed method outperforms the state-of-the-art unsup-SimCSE on a STS task. |
Measuring Robustness for NLP (2022.coling-1)
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| Challenge: | Existing methods to evaluate NLP models are limited to news domains and cannot be generalized to other domains. |
| Approach: | They propose a measure of NLP quality based on robustness . they measure consistency of cross-domain accuracy and introduce coefficient of variation and gamma-Robustness based upon human evaluation . |
| Outcome: | The proposed approach shows higher agreement with human evaluation than accuracy scores on ranking machine translation systems. |
CSL: A Large-scale Chinese Scientific Literature Dataset (2022.coling-1)
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| Challenge: | Existing datasets centered around the English language restrict development of Chinese scientific NLP. |
| Approach: | They present a large-scale Chinese scientific literature dataset based on Chinese papers . they use semi-structured data as a natural annotation for many supervised NLP tasks . |
| Outcome: | The proposed dataset can serve as a Chinese corpus and perform many supervised tasks. |
Singlish Message Paraphrasing: A Joint Task of Creole Translation and Text Normalization (2022.coling-1)
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| Challenge: | Existing computational approaches to translate languages or creoles back to standard English are challenging . lexical level normalization, syntactic level editing, and semantic level rewriting are key to a successful translation task. |
| Approach: | They propose a computational task to parse Singlish into English using its dialects . they propose to use a dataset to normalize and edit the text to improve translation . |
| Outcome: | The proposed model can improve translation performance and improve stance detection. |
CINO: A Chinese Minority Pre-trained Language Model (2022.coling-1)
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| Challenge: | Existing multilingual pre-trained language models do not perform well on some low-resource languages. |
| Approach: | They propose a multilingual pre-trained language model for Chinese minority languages . they collect documents from Wikipedia and construct two classification datasets . |
| Outcome: | The proposed model outperforms baseline models on various classification tasks. |
One Word, Two Sides: Traces of Stance in Contextualized Word Representations (2022.coling-1)
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| Challenge: | a Lexical Semantic Change study examines the way we use words . it focuses on the use of words by people who disagree on a particular topic . |
| Approach: | They examine whether word embeddings reflect the way we use words . they use BERT embeddables from datasets with stance annotations to examine this question . |
| Outcome: | The results show that people with opposing stances use different words when talking about a topic . the results are not related to studies that investigate the usage of specific words across different viewpoints. |
Prepositions Matter in Quantifier Scope Disambiguation (2022.coling-1)
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Aleksander Leczkowski, Justyna Grudzińska, Manuel Vargas Guzmán, Aleksander Wawer, Aleksandra Siemieniuk
| Challenge: | Existing work on how to integrate world knowledge into a QSD model has been limited . |
| Approach: | They use a scope-disambiguated corpus annotated with prepositional senses to integrate our knowledge into a machine learning model. |
| Outcome: | The proposed model is based on a scope-disambiguated corpus annotated with prepositional senses . Statistical analysis shows that prepositions have a positive impact on the learnability of automatic QSD systems. |
Modelling Commonsense Properties Using Pre-Trained Bi-Encoders (2022.coling-1)
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| Challenge: | Pre-trained language models can capture commonsense properties that are rarely expressed in text. |
| Approach: | They propose to fine-tune language models to explicitly model commonsense properties . they train separate concept and property encoders on extracted hyponym-hypernym pairs and generic sentences . |
| Outcome: | The proposed model can capture commonsense properties with higher accuracy than human models . a new study shows that the model can model commonsensence properties with much higher accuracy . |
COIN – an Inexpensive and Strong Baseline for Predicting Out of Vocabulary Word Embeddings (2022.coling-1)
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| Challenge: | Word embedding models only include terms that occur a sufficient number of times in training corpora. |
| Approach: | They propose a method for predicting word embeddings for out of vocabulary terms using word2vec. |
| Outcome: | The proposed method surpasses several methods on benchmark tasks and is inexpensive to compute. |
DynGL-SDP: Dynamic Graph Learning for Semantic Dependency Parsing (2022.coling-1)
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| Challenge: | Existing parsers that learn graph representations based on static graphs are error-prone and disjointed . Graph-based parser can parse sentences efficiently but suffer from error propagation . |
| Approach: | They propose a dynamic graph learning framework to learn graph representations based on a static graph constructed by an existing parser. |
| Outcome: | The proposed parser outperforms the previous parsers on the SemEval-2015 task 18 dataset in three languages. |
Knowledge Is Flat: A Seq2Seq Generative Framework for Various Knowledge Graph Completion (2022.coling-1)
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| Challenge: | Knowledge Graph Completion (KGC) has been extended to multiple knowledge graph (KG) structures, initiating new research directions, e.g. static KGC, temporal KGC and few-shot KGC. |
| Approach: | They propose a generative framework that could tackle different verbalizable graph structures by unifying the representation of KG facts into "flat" text. |
| Outcome: | The proposed framework outperforms many competitive baselines and sets new state-of-the-art performance on five benchmarks. |
Modelling Frequency, Attestation, and Corpus-Based Information with OntoLex-FrAC (2022.coling-1)
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| Challenge: | OntoLex-Lemon has become a de facto standard for lexical resources in the web of data. |
| Approach: | This paper provides the first overall description of the emerging OntoLex module for Frequency, Attestations, and Corpus-Based Information. |
| Outcome: | This paper provides the first overall description of the emerging OntoLex module for Frequency, Attestations, and Corpus-Based Information (OntoLx-FrAC) it is intended to complement OntoLemon with the vocabulary to represent major types of information found in or automatically derived from corpora, for applications in both language technology and the language sciences. |
Contrast Sets for Stativity of English Verbs in Context (2022.coling-1)
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| Challenge: | Current methods for classifying verbs in context as dynamic or stative are limited to particular data sets. |
| Approach: | They apply contrast set methodology to classify verbs in context as dynamic or stative . they create nearly 300 contrastive pairs by perturbing test set instances just enough to change their labels . |
| Outcome: | The contrast set method is used to evaluate the performance of a model on a classifying task . the model performs worse on transformed examples than on human examples . |
Multilingual and Multimodal Topic Modelling with Pretrained Embeddings (2022.coling-1)
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| Challenge: | a novel neural topic model for comparable data maps texts from multiple languages and images into a shared topic space. |
| Approach: | They propose a novel multimodal multilingual neural topic model that maps texts from multiple languages and images into a shared topic space. |
| Outcome: | The proposed model outperforms a zero-shot topic model in predicting topic distributions for comparable multilingual data and performs as well on unaligned embeddings as it does on aligned embeds. |
Zero-shot Script Parsing (2022.coling-1)
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| Challenge: | Existing resources cover only a small number of tasks, limiting its practical usefulness. |
| Approach: | They propose a zero-shot learning approach to script parsing which enables us to acquire script knowledge without domain-specific annotations. |
| Outcome: | The proposed model outperforms a previous model with scenario-specific supervision and achieves 68.1/74.4 average F1 for event / participant parsing. |
Word Sense Disambiguation with Knowledge-Enhanced and Local Self-Attention-based Extractive Sense Comprehension (2022.coling-1)
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| Challenge: | Word sense disambiguation (WSD) is one of the most challenging tasks in natural language processing. |
| Approach: | They propose a method to extract the right sense from a sentence context . they propose to incorporate additional examples and definitions of related senses in WordNet . |
| Outcome: | The proposed method achieves better performance than baseline models on public benchmark datasets. |
A Novel Multi-Task Learning Approach for Context-Sensitive Compound Type Identification in Sanskrit (2022.coling-1)
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Jivnesh Sandhan, Ashish Gupta, Hrishikesh Terdalkar, Tushar Sandhan, Suvendu Samanta, Laxmidhar Behera, Pawan Goyal
| Challenge: | Previously, compounding is a problem of identifying semantic relations between components of a word. |
| Approach: | They propose a multi-task learning architecture which incorporates contextual information and enriches syntactic information using morphological tagging and dependency parsing as auxiliary tasks. |
| Outcome: | The proposed architecture shows 6.1 points accuracy and 7.7 points (F1-score) absolute gain in English and Marathi languages. |
Testing Large Language Models on Compositionality and Inference with Phrase-Level Adjective-Noun Entailment (2022.coling-1)
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| Challenge: | Existing studies have shown that pre-trained large language models acquire knowledge during pre-training which enables reasoning over relationships between words and more complex inferences over larger units of meaning. |
| Approach: | They propose a benchmark to test compositional entailment models using adjective-noun phrases. |
| Outcome: | The proposed model can generalise well to out–of–distribution sets, since the required knowledge can be stored in the representations of subwords (SW) tokens. |
Does BERT Recognize an Agent? Modeling Dowty’s Proto-Roles with Contextual Embeddings (2022.coling-1)
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| Challenge: | Contextual embeddings build multidimensional representations of word tokens based on their context of occurrence. |
| Approach: | They propose to map the verb embeddings to an interpretable space of semantic properties built from a linguistic dataset and test their ability to model the semantic properties of the agent of the verbs participating in the alternation. |
| Outcome: | The proposed models can model the semantic properties of the verbs participating in the so-called causative alternation. |
Towards Structure-aware Paraphrase Identification with Phrase Alignment Using Sentence Encoders (2022.coling-1)
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| Challenge: | Existing paraphrase identification datasets exhibit high correlation between positive pairs and the degree of their lexical overlap. |
| Approach: | They propose to combine sentence encoders with an alignment component by representing each sentence as a list of predicate-argument spans and decomposing the sentence-level meaning comparison into the alignment between their spans. |
| Outcome: | The proposed approach improves performance and interpretability for various sentence encoders. |
CILex: An Investigation of Context Information for Lexical Substitution Methods (2022.coling-1)
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| Challenge: | Existing methods for lexical substitution rely on manually curated lexicals and contextual word embedding models. |
| Approach: | They propose a method that uses contextual sentence embeddings to generate substitutes for a target word given a context and a model that captures additional context information complimenting contextual word embedders. |
| Outcome: | The proposed method is state-of-the-art on the widely used LS07 and CoInCo datasets with P@1 scores of 55.96% and 57.25% for lexical substitution. |
Emotion Enriched Retrofitted Word Embeddings (2022.coling-1)
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| Challenge: | Word embeddings that encode lexical-semantic relations do not capture emotion aspects of words. |
| Approach: | They propose a retrofitting method to update the vectors of emotion bearing words . they find that the retrofitted embeddings achieve better distances between clusters . |
| Outcome: | The proposed method achieves better distances between clusters and clusters for words having the same emotions. |
Metaphor Detection via Linguistics Enhanced Siamese Network (2022.coling-1)
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| Challenge: | Empirical results indicate that MisNet achieves competitive performance on several datasets. |
| Approach: | They propose a model that converts linguistic rules into semantic matching tasks. |
| Outcome: | Empirical results show that MisNet achieves competitive performance on several datasets. |
Fast and Accurate End-to-End Span-based Semantic Role Labeling as Word-based Graph Parsing (2022.coling-1)
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| Challenge: | Using end-to-end span-based SRL, we propose a word-based graph parsing task for word-level representation of spans . compared with word-driven SRL, span-Based SRL is more complex due to difficulties in determining argument boundaries. |
| Approach: | They propose to cast end-to-end span-based SRL as a word-based graph parsing task . they propose a constrained Viterbi procedure to ensure the legality of the output graph . |
| Outcome: | The proposed model can parse 669/252 sentences per second without and with pre-trained models. |
Unsupervised Lexical Substitution with Decontextualised Embeddings (2022.coling-1)
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| Challenge: | Existing methods for lexical substitution using pre-trained language models have some limitations. |
| Approach: | They propose an unsupervised method for lexical substitution using pre-trained language models. |
| Outcome: | The proposed method outperforms baseline models and establishes a state-of-the-art without supervision or fine-tuning. |
Transparent Semantic Parsing with Universal Dependencies Using Graph Transformations (2022.coling-1)
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| Challenge: | Existing semantic parsers are based on deep learning, but rule-based approaches offer advantages . a drawback of neural semantic parses is that their output lacks explainability . |
| Approach: | They propose a method that maps a syntactic dependency tree to a formal meaning representation using a series of graph transformations. |
| Outcome: | The proposed method outperforms neural parsers in English, German, Italian and Dutch. |
Multilingual Word Sense Disambiguation with Unified Sense Representation (2022.coling-1)
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| Challenge: | Existing researches on word sense disambiguation focus on English only. |
| Approach: | They propose to build knowledge and supervised based multilingual word sense disambiguation systems on a multilingual lexicon describing the same set of concepts across languages. |
| Outcome: | The proposed model can understand the fine-grained semantics of words under specific contexts. |
A Transition-based Method for Complex Question Understanding (2022.coling-1)
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| Challenge: | Existing work on complex question understanding does not model intermediate states and does not provide step-wise information. |
| Approach: | They propose a transition-based method where a decider predicts a sequence of actions to build the graph node-by-node. |
| Outcome: | The proposed method parses complex questions to QDMR using atomic operators . it has transparent and human-readable intermediate results, showing improved interpretability . |
Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures inside Arguments (2022.coling-1)
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| Challenge: | Recent works of SRL mainly fall into two lines: 1) BIO-based; 2) span-based. |
| Approach: | They propose to regard flat argument spans as latent subtrees, thus reducing SRL to a tree parsing task. |
| Outcome: | The proposed model performs better than previous syntax-agnostic models on CoNLL05 and CoNll12 benchmarks. |
Noisy Label Regularisation for Textual Regression (2022.coling-1)
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| Challenge: | Existing methods to regularise noisy labels are ineffective in the face of noisy data. |
| Approach: | They propose a method that regularises noisy labels and prevents error propagation from the input layer. |
| Outcome: | The proposed method regularises noisy labels and improves generalisation performance over real-world human-disagreement annotations and randomly-corrupted and data-augmented labels. |
Detecting Suicide Risk in Online Counseling Services: A Study in a Low-Resource Language (2022.coling-1)
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| Challenge: | Existing domain-specific models for detecting suicide are lacking in low-resource languages. |
| Approach: | They propose a model that combines pre-trained language models with a fixed set of suicidal cues and a two-stage fine-tuning process to detect SI. |
| Outcome: | The proposed model outperforms baseline models even early on in the conversation and performs well across genders and age groups. |
Does Meta-learning Help mBERT for Few-shot Question Generation in a Cross-lingual Transfer Setting for Indic Languages? (2022.coling-1)
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Aniruddha Roy, Rupak Kumar Thakur, Isha Sharma, Ashim Gupta, Amrith Krishna, Sudeshna Sarkar, Pawan Goyal
| Challenge: | Existing approaches to few-shot Question Generation (QG) are limited and require manual annotation. |
| Approach: | They propose to use multilingual BERT to perform few-shot question generation with cross-lingual transfer. |
| Outcome: | The proposed model improves in few-shot QG and human evaluation confirms it. |
Revisiting Syllables in Language Modelling and Their Application on Low-Resource Machine Translation (2022.coling-1)
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| Challenge: | Language modelling and machine translation tasks mostly use subword or character inputs, but syllables are rarely used. |
| Approach: | They explore the potential of syllables for open-vocabulary language modelling in 21 languages. |
| Outcome: | The proposed method outperforms characters and subwords in a non-related and low-resource language pair. |
Aligning Multilingual Embeddings for Improved Code-switched Natural Language Understanding (2022.coling-1)
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| Challenge: | a recent study has shown that multilingual models can be effective on monolingual data but need additional training to work well with code-switched text. |
| Approach: | They propose to train multilingual models with alignment objectives using parallel text . they find such an explicit alignment step improves performance on code-switched NLP tasks . |
| Outcome: | The proposed model improves on Hindi-English Sentiment Analysis, Named Entity Recognition and Question Answering tasks. |
Fashioning Local Designs from Generic Speech Technologies in an Australian Aboriginal Community (2022.coling-1)
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| Challenge: | Recent research has focused on low-resource languages and the transcription bottleneck paradigm. |
| Approach: | They propose to use a spoken term detection system to train a speech recognition system in an Aboriginal community to reach better comprehension and engagement from Aboriginal participants. |
| Outcome: | The proposed system can be implemented in an Aboriginal community and reach better comprehension and engagement from Aboriginal participants. |
Few-Shot Pidgin Text Adaptation via Contrastive Fine-Tuning (2022.coling-1)
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| Challenge: | Currently, low resource languages are not supported by proper translation systems or parallel corpus. |
| Approach: | They propose to fine-tune the pretrained language models to generate utterances in English-to-Pidgin by leveraging the proximity of the source and target languages and using positive and negative examples in constrastive training objectives. |
| Outcome: | The proposed method is sufficient to generate utterances in English-to-Pidgin, which are two closely-related languages. |
Penalizing Divergence: Multi-Parallel Translation for Low-Resource Languages of North America (2022.coling-1)
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| Challenge: | Existing studies show that multi-parallel translation models can overfit when training data are limited. |
| Approach: | They introduce a regularizer which penalizes translation models when they represent source sentences with identical target translations in divergent ways. |
| Outcome: | The proposed model improves when the target data for all language pairs are identical. |
Assessing Digital Language Support on a Global Scale (2022.coling-1)
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| Challenge: | a new method is being developed to assess how well each language is doing in terms of digital language support. |
| Approach: | They develop an automated method to assess how well each language is doing in terms of digital language support. |
| Outcome: | The proposed method scrapes the names of supported languages from 143 digital tools and produces an explainable model for quantifying and monitoring it on a global scale. |
Persian Natural Language Inference: A Meta-learning Approach (2022.coling-1)
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| Challenge: | In general, shared representations are learned separately, either across tasks or across languages. |
| Approach: | They propose a meta-learning approach for inferring natural language in Persian . they use different task information or other language information to form additional high-quality tasks . |
| Outcome: | The proposed method outperforms the baseline approach, improving accuracy by roughly six percent. |
Global Readiness of Language Technology for Healthcare: What Would It Take to Combat the Next Pandemic? (2022.coling-1)
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| Challenge: | Language Technology (LT) has been used in the COVID-19 pandemic, but only in a handful of languages. |
| Approach: | They propose to use conversational agents for information dissemination and basic diagnosis in 15 Asian and African languages with varying resource-availability to test their knowledge of LT. |
| Outcome: | The proposed research confirms the pitiful state of LT even for languages with large speaker bases, such as Sinhala and Hausa, and identifies the gaps that could help prioritize research and investment strategies in LT for healthcare. |
Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning (2022.coling-1)
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| Challenge: | Multilingual pre-trained language models have shown impressive performance on several downstream tasks for both high-resourced and low-resource languages. |
| Approach: | They propose to apply multilingual adaptive fine-tuning to 17 most-resourced African languages and three other high-resource languages to encourage cross-lingual transfer learning. |
| Outcome: | The proposed approach is competitive to LAFT on individual languages while requiring significantly less disk space. |
Noun Class Disambiguation in Runyankore and Related Languages (2022.coling-1)
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| Challenge: | Bantu languages are still computationally under-resourced due to their complex grammatical structure . morphological analyzers, text generation tools and a morphology analyzer are among the tools used . |
| Approach: | They propose a syntactic and semantic method to disambiguate among singular nouns . they use the nearest neighbors of a query word as semantic generalizations based on Runyankore . |
| Outcome: | The proposed method improves accuracy in three Bantu languages compared to using only the syntactic or semantic approach. |
Improving Low-resource RRG Parsing with Cross-lingual Self-training (2022.coling-1)
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| Challenge: | a theoretical framework for low-resource parsing is understudied in computational linguistics but widely used in typological research . a novel approach uses Role and Reference Grammar to parse low-source languages . |
| Approach: | They propose to extend an existing RRG parser into a cross-lingual parsing model . they also adopt self-training to adapt the model to a related language with no trees . |
| Outcome: | The proposed model extends into a cross-lingual parser, and iteratively expands the training data. |
A Simple and Effective Method to Improve Zero-Shot Cross-Lingual Transfer Learning (2022.coling-1)
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Kunbo Ding, Weijie Liu, Yuejian Fang, Weiquan Mao, Zhe Zhao, Tao Zhu, Haoyan Liu, Rong Tian, Yiren Chen
| Challenge: | Existing zero-shot cross-lingual transfer methods rely on parallel corpora or bilingual dictionaries . however, its effect is limited by the gap between embedding clusters of different languages . |
| Approach: | They propose Embedding-Push, Attention-Pull, and Robust targets to transfer English embeddings to virtual multilingual embedders without semantic loss. |
| Outcome: | Experimental results show that the proposed method outperforms existing methods on cross-lingual tasks and can achieve a better multilingual alignment. |
Towards Multi-Sense Cross-Lingual Alignment of Contextual Embeddings (2022.coling-1)
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| Challenge: | Existing approaches to learn cross-lingual word embeddings are sense agnostic . a novel framework to align contextual embeddables at the sense level is proposed . |
| Approach: | They propose a framework to align contextual embeddings at the sense level by leveraging cross-lingual signal from bilingual dictionaries only. |
| Outcome: | The proposed framework improves word sense disambiguation tasks by leveraging bilingual dictionaries . compared with baseline results, the proposed models achieve 0.52%, 2.09% and 1.29% performance improvements . |
How to Parse a Creole: When Martinican Creole Meets French (2022.coling-1)
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| Challenge: | a lack of annotated gold standard data is a major challenge for underresourced languages. |
| Approach: | They propose to use a French treebank to develop a dependency parser for Martinican Creole. |
| Outcome: | The proposed model is based on a French treebank and has 80 Martinican Creole sentences. |
Byte-based Multilingual NMT for Endangered Languages (2022.coling-1)
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| Challenge: | Existing work has not studied how byte encoding can benefit endangered languages . multilingual neural machine translation (MNMT) models suffer from out-of-vocabulary issues and representation bottleneck . |
| Approach: | They propose a multilingual multilingual neural machine translation system to alleviate the representation bottleneck and improve translation performance in endangered languages. |
| Outcome: | The proposed system outperforms subword-based models on twelve languages up to +18.5 BLEU points, an 840% relative improvement over baseline models. |
BRCC and SentiBahasaRojak: The First Bahasa Rojak Corpus for Pretraining and Sentiment Analysis Dataset (2022.coling-1)
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| Challenge: | Code-mixing is prevalent in multilingual societies and is challenging to train . we use data augmentation to build a model to deal with code-mixed inputs . |
| Approach: | They propose to train a model to deal with code-mixing phenomena of Bahasa Rojak using data augmentation to construct a Bahasan Rojakin corpus and a pre-trained model to process input tokens. |
| Outcome: | The proposed model can tag the language of the input token automatically to process code-mixing input. |
WordNet-QU: Development of a Lexical Database for Quechua Varieties (2022.coling-1)
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| Challenge: | Quechua is a low-resource language from south America but lacks resources to build high-performance computational systems. |
| Approach: | They propose to include Quechua in a lexical database called wordnet . they propose a synset alignment algorithm to compare Quechuan to its nearest high-resource language . |
| Outcome: | The proposed system compares Quechua to its nearest high-resource language, Spanish . it uses a synset alignment algorithm to find Quechuan resources in a lexical database . |
When the Student Becomes the Master: Learning Better and Smaller Monolingual Models from mBERT (2022.coling-1)
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| Challenge: | Using a jointly trained model for 102 languages, monolingual models outperform the original model. |
| Approach: | They propose to distill monolingual models from a jointly trained model for 102 languages using a text corpus of 160 GB. |
| Outcome: | The proposed model outperforms the original model for 6 languages with varying amounts of resources and language families. |
Zero-shot Disfluency Detection for Indian Languages (2022.coling-1)
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| Challenge: | Disfluency correction models can help alleviate this problem, but the unavailability of labeled data in low-resource languages impairs progress. |
| Approach: | They propose to use a pretrained multilingual model to detect zero-shot disfluency in Indian languages. |
| Outcome: | The proposed model achieves F1 scores of 75 and higher on five disfluency types across four languages. |
Evaluating Word Embeddings in Extremely Under-Resourced Languages: A Case Study in Bribri (2022.coling-1)
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| Challenge: | a case study of word embeddings in an under-resourced context is presented . Embeddings are critical for many NLP tasks, but their evaluation in actual under-sourced settings needs further investigation. |
| Approach: | They adapt word embeddings to an under-resourced indigenous language in Bribri . they find that the best models find the appropriate semantic target 60% of the time . |
| Outcome: | The proposed models were adapted to an under-resourced English language . the best models found the appropriate semantic target 60% of the time . |
Applying Natural Annotation and Curriculum Learning to Named Entity Recognition for Under-Resourced Languages (2022.coling-1)
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| Challenge: | Existing approaches to build NLP models for low-resourced languages rely on machine translation or cross-lingual transfer. |
| Approach: | They propose to use natural annotations to build synthetic training sets from resources not originally designed for the target downstream task. |
| Outcome: | The proposed model achieves the F1 score of 0.78 for Belarusian starting from zero resources compared to the baseline of 0.63 for English . the proposed model can be fine-tuned to reflect linguistic properties, such as the grammatical case and gender, for the Slavic languages. |
Taking Actions Separately: A Bidirectionally-Adaptive Transfer Learning Method for Low-Resource Neural Machine Translation (2022.coling-1)
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| Challenge: | Existing approaches to train NMT models rely on sparse parallel data . a variety of PC variants yield significant improvements for low-resource NMT . |
| Approach: | They propose to transfer well-trained NMT models to low-resource languages by bidirectionally-adaptive learning strategy . they divide inner constituents of Parent encoder into two "teams" aiming to adapt to characteristics of low- and high-resourced languages . |
| Outcome: | The proposed method improves on low-resource NMT models with a variety of PC variants. |
HCLD: A Hierarchical Framework for Zero-shot Cross-lingual Dialogue System (2022.coling-1)
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| Challenge: | Existing methods to train task-oriented dialogue systems in monolingual datasets are expensive to build. |
| Approach: | They propose a hierarchical framework to classify intents in high-level and slot filling in low-level . they incorporate sentence-level alignment among different languages to enhance intent detection . |
| Outcome: | The proposed framework achieves the performance on a public task-oriented dialog dataset. |
GraDA: Graph Generative Data Augmentation for Commonsense Reasoning (2022.coling-1)
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| Challenge: | Recent advances in commonsense reasoning have been fueled by the availability of large-scale human annotated datasets. |
| Approach: | They propose a graph-generative data augmentation framework to synthesize factual data samples from knowledge graphs for commonsense reasoning. |
| Outcome: | The proposed framework improves SocialIQA, CODAH, HellaSwag and CommonsenseQA . it also performs well for generative tasks like ProtoQA proving its robustness to adversaries . |
Eureka: Neural Insight Learning for Knowledge Graph Reasoning (2022.coling-1)
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| Challenge: | Existing knowledge embedding methods have limited performance on knowledge graph reasoning tasks . eureka is empowered to learn seen relations with sufficient training triples . |
| Approach: | They propose a neural insight learning framework called Eureka to bridge the “seen” to “unsea” gap . Eureca is empowered to learn seen relations with sufficient training triples while providing flexibility to learn unseen relations given only one trigger . |
| Outcome: | The proposed framework outperforms state-of-the-art models on seen and unseen relations . it can learn seen and unseen relationships with sufficient training triples . |
CitRet: A Hybrid Model for Cited Text Span Retrieval (2022.coling-1)
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| Challenge: | Current methods for citing text span retrieval (CTSR) rely on pre-trained off-the-shelf deep learning models like SciBERT. |
| Approach: | They propose a hybrid model for cited text span retrieval that leverages unique semantic and syntactic structural characteristics of scientific documents. |
| Outcome: | The proposed model improves state-of-the-art by 15% on the CLSciSumm shared tasks. |
A Weak Supervision Approach for Predicting Difficulty of Technical Interview Questions (2022.coling-1)
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| Challenge: | Existing models require large volumes of candidate response data to train . Existing approaches require large amounts of candidate data to generate questions and generate models. |
| Approach: | They create a dataset of interview questions with difficulty scores for deep learning and use it to evaluate SOTA models trained using weak supervision. |
| Outcome: | The proposed model improves the difficulty and promise of weak supervision for interview questions and identifies the potential for weak supervision. |
Reinforcement Learning with Large Action Spaces for Neural Machine Translation (2022.coling-1)
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| Challenge: | Recent work has argued that the gains produced by Reinforcement learning are mostly due to promoting tokens that have already received a fairly high probability in pre-training. |
| Approach: | They hypothesize that the large action space is a main obstacle to RL’s effectiveness in MT by reducing the size of the vocabulary without changing the vocabulary. |
| Outcome: | The proposed method improves by 1.5 BLEU points on average. |
Noise Learning for Text Classification: A Benchmark (2022.coling-1)
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| Challenge: | Existing noise learning methods for text classification are underdeveloped . authors propose a noise learning benchmark for text classification . |
| Approach: | They propose to use four state-of-the-art methods of noise learning from the image domain to classify text. |
| Outcome: | The proposed benchmark of noise learning for text classification is based on four methods and five noise modes. |
Mitigating the Diminishing Effect of Elastic Weight Consolidation (2022.coling-1)
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| Challenge: | Existing work addresses catastrophic forgetting in sequential training by fine-tuning pre-trained language models on different datasets. |
| Approach: | They propose to rescale the components of EWC to mitigate catastrophic forgetting by mixing new and old training data and retraining the model from scratch. |
| Outcome: | The proposed method requires smaller values for the trade-off parameters to achieve comparable results to EWC on natural language inference and fact-checking tasks. |
Token and Head Adaptive Transformers for Efficient Natural Language Processing (2022.coling-1)
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| Challenge: | Pre-trained language models like BERT have shown significant accuracy improvements on various tasks, but their computational cost and memory footprint are prohibitive. |
| Approach: | They propose to extend Length Adaptive Transformer to extend the model to a token and head pruning scheme to optimize pruning efficiency. |
| Outcome: | The proposed model can compress and accelerate BERT-based models by fine-tuning and a token and head pruning scheme. |
Don’t Judge a Language Model by Its Last Layer: Contrastive Learning with Layer-Wise Attention Pooling (2022.coling-1)
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| Challenge: | Recent pre-trained language models (PLMs) have shown competitive performance on many natural language processing tasks. |
| Approach: | They propose a pooling strategy which preserves layer-wise signals captured in each layer and learns digested linguistic features for downstream tasks. |
| Outcome: | The proposed method improves on standard semantic textual similarity and semantic search tasks. |
SHAP-Based Explanation Methods: A Review for NLP Interpretability (2022.coling-1)
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| Challenge: | Existing models with opacity problems have been proposed to address this problem. |
| Approach: | They propose a unified local-interpretability framework with a rigorous theoretical foundation on the game-theoretic concept of Shapley values. |
| Outcome: | The proposed framework is based on the Shapley-value-based model explanations. |
A Simple Log-based Loss Function for Ordinal Text Classification (2022.coling-1)
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| Challenge: | Existing methods for ordinal text classification do not incorporate ordinal character into their feedback. |
| Approach: | They propose a new ordinal log-loss loss function that incorporates ordinal character into its feedback. |
| Outcome: | The proposed loss function outperforms state-of-the-art methods on four benchmark text classification datasets. |
Ask Question First for Enhancing Lifelong Language Learning (2022.coling-1)
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| Challenge: | Existing approaches to stream learning NLP tasks suffer from catastrophic forgetting and are exacerbated when the previous task’s pseudo data is insufficient. |
| Approach: | They propose to use a new data format to train pseudo questions of previous tasks to stream learning NLP tasks while retaining knowledge of previous ones. |
| Outcome: | The proposed model is more robust to sufficient and insufficient pseudo-data when the task boundary is both clear and unclear. |
DoubleMix: Simple Interpolation-Based Data Augmentation for Text Classification (2022.coling-1)
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| Challenge: | Existing methods to improve the robustness of text classification models are token-, sentence-, and hiddenlevel augmentation. |
| Approach: | They propose an interpolation-based data augmentation approach called DoubleMix to improve the robustness of text classification models by learning the “shifted” features in hidden space. |
| Outcome: | The proposed approach outperforms several popular methods on six text classification benchmark datasets and visual analysis shows that the model features are highly interpretable. |
Large Sequence Representation Learning via Multi-Stage Latent Transformers (2022.coling-1)
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| Challenge: | a novel algorithm for named-entity recognition (NER) uses language and spatial features to predict entity tags for structured text . a dataset of 11,926 images depicting food product labels is used to perform NER tasks . |
| Approach: | They propose a multi-stage transformer architecture for named-entity recognition . they propose RADAR, an LSTM classifier operating at character level, to refine NER predictions . |
| Outcome: | The proposed method outperforms two competing models on a food label dataset. |
MockingBERT: A Method for Retroactively Adding Resilience to NLP Models (2022.coling-1)
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| Challenge: | Existing remediations have compromised accuracy or required full model re-training with each new class of attacks. |
| Approach: | They propose a method of retroactively adding resilience to misspellings to transformer-based NLP models and propose generating adversarial misspells using an approximate method. |
| Outcome: | The proposed method significantly reduces the cost needed to evaluate a model’s resilience to adversarial attacks. |
Equivariant Transduction through Invariant Alignment (2022.coling-1)
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| Challenge: | Existing studies have demonstrated that NLP models possess the ability to generalize compositionally, but none have tested it. |
| Approach: | They propose to use a group-equivariant neural network to encode an inductive bias for SCAN to test for this ability. |
| Outcome: | The proposed architecture outperforms existing group-equivariant approaches on the SCAN task and shows that it can generalize compositionally. |
Where Does Linguistic Information Emerge in Neural Language Models? Measuring Gains and Contributions across Layers (2022.coling-1)
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| Challenge: | Probing studies have explored where in neural language models linguistic information is located . standard approach is to focus on the layers whose representations give the highest performance on probing tasks . |
| Approach: | They propose a method that asks where task-relevant information emerges in the model by focusing on the layers that give the highest performance. |
| Outcome: | The proposed method confirms the expected ordering only for one of the pairs, indicating that the features that contribute the most to probing tasks are not as high-level as global metrics suggest. |
Accelerating Inference for Pretrained Language Models by Unified Multi-Perspective Early Exiting (2022.coling-1)
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| Challenge: | Existing competitive methods to accelerate inference of pretrained language models are limited by their complexity and computational consumption. |
| Approach: | They propose a unified horizontal and vertical multi-perspective early exiting framework to accelerate inference of transformer-based models. |
| Outcome: | Experiments show that MPEE can achieve higher acceleration inference with competent performance than existing competitive methods. |
Topology Imbalance and Relation Inauthenticity Aware Hierarchical Graph Attention Networks for Fake News Detection (2022.coling-1)
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| Challenge: | Existing methods to detect fake news focus on mining lexical and syntactic features. |
| Approach: | They propose a topology imbalance and Relation inauthenticity aware Hierarchical Graph Attention Networks to identify fake news on social media. |
| Outcome: | The proposed method outperforms state-of-the-art methods on real-world datasets. |
Temporal Knowledge Graph Completion with Approximated Gaussian Process Embedding (2022.coling-1)
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| Challenge: | Existing TKGC methods are based on deterministic vector embeddings, which are not flexible and expressive enough. |
| Approach: | They propose a method that maps entities and relations to multivariate Gaussian processes by mapping global trends and local fluctuations in TKGs. |
| Outcome: | The proposed method can predict global trends and local fluctuations in the TKGs and can be optimized on two real-world benchmark datasets. |
CILDA: Contrastive Data Augmentation Using Intermediate Layer Knowledge Distillation (2022.coling-1)
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Md Akmal Haidar, Mehdi Rezagholizadeh, Abbas Ghaddar, Khalil Bibi, Phillippe Langlais, Pascal Poupart
| Challenge: | Knowledge distillation (KD) is an efficient framework for compressing large-scale pre-trained language models. |
| Approach: | They propose a data augmentation technique tailored for knowledge distillation based on contrastive loss to improve masked adversarial data augmented by intermediate layer matching. |
| Outcome: | The proposed technique outperforms state-of-the-art methods on the GLUE benchmark and in an out-of domain evaluation. |
Pro-KD: Progressive Distillation by Following the Footsteps of the Teacher (2022.coling-1)
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| Challenge: | Knowledge distillation (KD) is a powerful tool for deep learning applications. |
| Approach: | They propose a method which defines a smoother training path for the student by following the training footprints of the teacher rather than solely relying on distilling from a single mature fully-trained teacher. |
| Outcome: | The proposed technique is quite effective in mitigating the capacity-gap problem and the checkpoint search problem. |
Classical Sequence Match Is a Competitive Few-Shot One-Class Learner (2022.coling-1)
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| Challenge: | Existing models that use transformers are unable to learn new knowledge in the few-shot scenarios. |
| Approach: | They propose a few-shot one-class problem which takes a known sample as a reference to detect whether an unknown instance belongs to the same class. |
| Outcome: | The proposed method significantly outperforms transformer models under meta-learning and fine-tuning. |
Unsupervised Domain Adaptation for Text Classification via Meta Self-Paced Learning (2022.coling-1)
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| Challenge: | Recent methods addressing unsupervised domain adaptation for textual tasks extracted domain-invariant representations through balancing between multiple objectives to align feature spaces between source and target domains. |
| Approach: | They propose to use meta-learning framework to train a neural network-based self-paced learning procedure in an end-to-end manner. |
| Outcome: | The proposed method significantly improves performance on target domains, surpassing state-of-the-art approaches. |
WARM: A Weakly (+Semi) Supervised Math Word Problem Solver (2022.coling-1)
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| Challenge: | Existing approaches to solving math word problems require full supervision in the form of intermediate equations. |
| Approach: | They propose a weakly supervised model that requires only the final answer as supervision to solve math word problems. |
| Outcome: | The proposed model achieves accuracy gains of 4.5% and 32% over current weakly-supervised methods on standard Math23K and AllArith datasets. |
Attention Networks for Augmenting Clinical Text with Support Sets for Diagnosis Prediction (2022.coling-1)
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| Challenge: | Clinical language models may suffer from imbalanced vocabulary for describing diseases or symptoms. |
| Approach: | They propose to augment clinical text with potentially complementary diagnostic codes from prior admissions or as they emerge during differential diagnosis to improve the performance. |
| Outcome: | The proposed approach outperforms the previous state-of-the-art PubMedBERT by up 3% points. |
PARSE: An Efficient Search Method for Black-box Adversarial Text Attacks (2022.coling-1)
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| Challenge: | Neural networks are vulnerable to adversarial examples, i.e., under a black-box scenario. |
| Approach: | They propose a word-level search algorithm that searches for subareas under dynamic search space following the subarea importance. |
| Outcome: | The proposed algorithm can achieve comparable success rates to complex search methods while saving numerous queries and time. |
A Closer Look at Parameter Contributions When Training Neural Language and Translation Models (2022.coling-1)
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| Challenge: | Neural models and Transformers have been used for almost every NLP task . however, the intrinsic dynamics of the training procedure have not been studied in depth for highly complex network architectures. |
| Approach: | They analyze the learning dynamics of neural language and translation models using Loss Change Allocation indicator . they use a standard Transformer architecture to train a model with three learning objectives . |
| Outcome: | The proposed model is based on a standard model that is used for training tasks. |
KNOT: Knowledge Distillation Using Optimal Transport for Solving NLP Tasks (2022.coling-1)
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| Challenge: | Knowledge Distillation using Optimal Transport (KNOT) aims to distill the natural language semantic knowledge from multiple teacher networks to a student network. |
| Approach: | They propose to distill natural language semantic knowledge from multiple teacher networks to a student network by learning to minimize the optimal transport cost of its assigned probability distribution over the labels to the weighted sum of probabilities predicted by the (local) teacher models. |
| Outcome: | The proposed method shows improvements in the global model’s SD performance over the baseline across three NLP tasks while performing on par with Entropy-based distillation on standard accuracy and F1 metrics. |
An Information Minimization Based Contrastive Learning Model for Unsupervised Sentence Embeddings Learning (2022.coling-1)
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| Challenge: | Recent contrastive learning methods keep positive pairs similar and push negative pairs apart, which leads to redundant information in sentence embeddings. |
| Approach: | They propose a contrastive learning approach which maximizes mutual information and minimizes the information entropy between positive and negative instances. |
| Outcome: | The proposed model outperforms all previous competitors on supervised and unsupervised tasks. |
Learn2Weight: Parameter Adaptation against Similar-domain Adversarial Attacks (2022.coling-1)
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| Challenge: | Prior black-box adversarial attacks assume that attackers can observe output labels from target models based on selected inputs. |
| Approach: | They propose a black-box adversarial attack where an attacker can transfer adversarials to a target domain and cause poor performance in target model. |
| Outcome: | The proposed attack is effective against similar-domain adversarial examples compared to standard black-box defense methods such as adversarials training and defense distillation. |
Sentence-aware Adversarial Meta-Learning for Few-Shot Text Classification (2022.coling-1)
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| Challenge: | Existing studies fail to consider the importance of the semantic interaction between sentence features and neglect to enhance the generalization ability of the model to new tasks. |
| Approach: | They propose to integrate an adversarial network architecture into the meta-learning system and leverage cost-effective modules to build a few-shot classification framework called SaAML. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on four benchmark datasets. |
Reweighting Strategy Based on Synthetic Data Identification for Sentence Similarity (2022.coling-1)
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| Challenge: | obtaining large amounts of human-annotated datasets to train a sentence embedding model is difficult and expensive. |
| Approach: | They propose to train a classifier that identifies machine-written sentences and then use it to train an embedding model on synthetic data. |
| Outcome: | The proposed method outperforms baselines on four real-world datasets and generalizes well. |
MaxMatch-Dropout: Subword Regularization for WordPiece (2022.coling-1)
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| Challenge: | Existing subword regularization methods are specialized to a particular tokenizer type. |
| Approach: | They propose a subword regularization method for WordPiece that uses a maximum matching algorithm for tokenization. |
| Outcome: | The proposed method improves the performance of text classification and machine translation tasks as well as other subword regularization methods. |
Adaptive Meta-learner via Gradient Similarity for Few-shot Text Classification (2022.coling-1)
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| Challenge: | Existing methods for few-shot text classification suffer from overfitting due to the lack of matching between the few amount of samples and complicated models. |
| Approach: | They propose a method to improve model generalization ability to a new task by leveraging a meta-learner via gradient similarity method. |
| Outcome: | The proposed method improves few-shot text classification performance on several benchmarks. |
Vocabulary-informed Language Encoding (2022.coling-1)
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| Challenge: | A Multilingual model relies on language encodings to identify input languages . a method to compute a vocabulary-informed language coding can improve multilingual models . |
| Approach: | They propose a method to compute a vocabulary-informed language encoding as the language representation for a required language. |
| Outcome: | The proposed method improves performance on unsupervised translation and cross-lingual embedding. |
OpticE: A Coherence Theory-Based Model for Link Prediction (2022.coling-1)
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| Challenge: | Knowledge representation learning is a key step required for link prediction tasks with knowledge graphs (KGs). |
| Approach: | They propose a new embedding approach based on the physical phenomenon of optical interference to reduce the semantic ambiguity in KGs. |
| Outcome: | The proposed model can compete with existing methods on KG benchmarks. |
Smoothed Contrastive Learning for Unsupervised Sentence Embedding (2022.coling-1)
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| Challenge: | Unsupervised contrastive sentence embedding models use InfoNCE loss function . increasing batch size leads to performance degradation when it exceeds threshold . |
| Approach: | They propose a simple smoothing strategy upon the InfoNCE loss function to reduce the number of false-negative pairs in a batch without increasing the batch size. |
| Outcome: | The proposed smoothing strategy improves unsupervised SimCSE on semantic similarity tasks. |
Knowledge Distillation with Reptile Meta-Learning for Pretrained Language Model Compression (2022.coling-1)
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| Challenge: | Knowledge distillation (KD) can transfer knowledge from the original model into a compact model to achieve model compression. |
| Approach: | They propose a knowledge distillation method with reptile meta-learning to facilitate the transfer of knowledge from the teacher to the student. |
| Outcome: | Extensive experiments on the GLUE benchmark show the proposed method performs better than previous methods. |
RotateCT: Knowledge Graph Embedding by Rotation and Coordinate Transformation in Complex Space (2022.coling-1)
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| Challenge: | Existing knowledge graph embedding methods fail to model non-commutative composition patterns . Existing methods are limited to complex space, resulting in a large number of parameters. |
| Approach: | They propose a knowledge graph embedding method that transforms the coordinates of each entity and then represents each relation as a rotation from head entity to tail entity in complex space. |
| Outcome: | The proposed method outperforms state-of-the-art methods on link prediction and path query answering. |
Can Data Diversity Enhance Learning Generalization? (2022.coling-1)
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| Challenge: | a diversity advanced actor-critical reinforcement learning framework is used to improve NLP generalization and accuracy. |
| Approach: | They introduce Diversity Advanced Actor-Critic reinforcement learning framework to improve NLP generalization and accuracy. |
| Outcome: | The proposed framework outperforms domain adaptation and generalization baselines without using any target domain knowledge. |
Generate-and-Retrieve: Use Your Predictions to Improve Retrieval for Semantic Parsing (2022.coling-1)
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Yury Zemlyanskiy, Michiel de Jong, Joshua Ainslie, Panupong Pasupat, Peter Shaw, Linlu Qiu, Sumit Sanghai, Fei Sha
| Challenge: | Existing retrieval techniques for semantic parsing use similarity of query and exemplar inputs . Existing work suggests that appending training samples to training samples improves performance . |
| Approach: | They propose a retrieval procedure that retrieves exemplars for which outputs are similar . existing retrieval techniques are based on similarity of query and exemplar inputs . |
| Outcome: | Existing retrieval techniques rely on similarity of query and exemplar inputs . they retrieve exemplars with similar outputs and generate a final prediction . |
Coarse-to-Fine: Hierarchical Multi-task Learning for Natural Language Understanding (2022.coling-1)
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Zhaoye Fei, Yu Tian, Yongkang Wu, Xinyu Zhang, Yutao Zhu, Zheng Liu, Jiawen Wu, Dejiang Kong, Ruofei Lai, Zhao Cao, Zhicheng Dou, Xipeng Qiu
| Challenge: | Existing methods to learn downstream tasks by stitches skill block lack rationality and interpretation. |
| Approach: | They propose a hierarchical framework with a coarse-to-fine paradigm for generalized text representations from the large-scale corpus. |
| Outcome: | The proposed model learns basic language properties from all tasks and boosts performance on relevant tasks. |
Automatic Label Sequence Generation for Prompting Sequence-to-sequence Models (2022.coling-1)
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| Challenge: | Prompting has shown to be sample efficient compared to fine-tuning with pre-trained models. |
| Approach: | They propose a fully automatic prompting method that uses natural language prompts on sequence-to-sequence models and a beam search method to generate a large amount of label sequence candidates. |
| Outcome: | The proposed method significantly outperforms other no-manual-design methods on single label words and generates large amount of label sequence candidates. |
Unsupervised Sentence Textual Similarity with Compositional Phrase Semantics (2022.coling-1)
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| Challenge: | Sentence Textual Similarity (STS) is a classic task that can be applied to downstream NLP applications such as text generation and retrieval. |
| Approach: | They propose a light-weighted Expectation-Correction (EC) formulation for STS computation. |
| Outcome: | The proposed approach is more efficient and scalable than previous approaches. |
A Generalized Method for Automated Multilingual Loanword Detection (2022.coling-1)
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Abhijnan Nath, Sina Mahdipour Saravani, Ibrahim Khebour, Sheikh Mannan, Zihui Li, Nikhil Krishnaswamy
| Challenge: | Loanwords are words incorporated from one language into another without translation . authors present a method to automatically detect loanwords across language pairs . |
| Approach: | They propose a method to automatically detect loanwords across language pairs . they incorporate edit distance, semantic similarity measures, phonetic alignment . |
| Outcome: | The proposed method outperforms existing methods on single-pair loanword detection tasks and can generalize to unseen language pairs with sufficient data. |
FeatureBART: Feature Based Sequence-to-Sequence Pre-Training for Low-Resource NMT (2022.coling-1)
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| Challenge: | FeatureBART is a linguistically motivated sequence-to-sequence monolingual pre-training strategy . syntactic features such as lemma, part-of-speech and dependency labels are incorporated into the pre-trained model . |
| Approach: | They propose a linguistically motivated sequence-to-sequence monolingual pre-training strategy that incorporates syntactic features into the framework. |
| Outcome: | The proposed model improves translation quality in bilingual and multilingual settings over models that do not use features. |
Multi-level Community-awareness Graph Neural Networks for Neural Machine Translation (2022.coling-1)
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| Challenge: | Recent studies have used Graph Neural Networks (GNNs) to encode language knowledge into token embeddings. |
| Approach: | They propose a multi-level community-awareness Graph Neural Network layer to jointly model local and global relationships between words and their linguistic roles in multiple communities. |
| Outcome: | The proposed method reduces time complexity in very long sentences while preserving the original meaning. |
On the Complementarity between Pre-Training and Random-Initialization for Resource-Rich Machine Translation (2022.coling-1)
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| Challenge: | Pre-Training (PT) of text representations has been successfully applied to low-resource Neural Machine Translation (NMT) however, it often fails to achieve notable gains on resource-rich NMT on par with its Random-Initialization (RI) counterpart. |
| Approach: | They propose to combine pre-training and random-initialization techniques to achieve significant improvements in NMT. |
| Outcome: | The proposed model fusion algorithm can achieve significant improvements on two resource-rich translation benchmarks. |
ngram-OAXE: Phrase-Based Order-Agnostic Cross Entropy for Non-Autoregressive Machine Translation (2022.coling-1)
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| Challenge: | Recent studies have incorporated approaches to improving the standard cross-entropy loss to ameliorate the effect of multimodality. |
| Approach: | They propose a new training oaxe loss which removes the penalty of word order errors in the standard cross-entropy loss. |
| Outcome: | Extensive experiments on NAT benchmarks show that the proposed approach improves translation quality and improves model performance. |
Language Branch Gated Multilingual Neural Machine Translation (2022.coling-1)
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| Challenge: | Existing approaches to multilingual neural machine translation do not allow knowledge transfer across languages. |
| Approach: | They propose a language branch gated multilingual neural machine translation module that encourages knowledge transfer within the same language branch. |
| Outcome: | The proposed approach significantly improves translation quality on middle- and low-resource languages over previous methods. |
Iterative Constrained Back-Translation for Unsupervised Domain Adaptation of Machine Translation (2022.coling-1)
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| Challenge: | Existing back-translation methods focus on in-domain lexical knowledge, which may lead to poor translation of unseen in- domain words. |
| Approach: | They propose an iterative constrained back-translation method to incorporate in-domain lexical knowledge into synthetic parallel data from BT. |
| Outcome: | The proposed method improves the BLEU score by up to 3.08 on four domains. |
Linguistically-Motivated Yorùbá-English Machine Translation (2022.coling-1)
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| Challenge: | Several phenomena where asymmetry arises have been identified as challenging problems for machine translation. |
| Approach: | They perform a fine-grained analysis of how an SMT system compares with two NMT systems when translating bare nouns into English. |
| Outcome: | The proposed model outperforms the SMT and BiLSTM models for 4 categories and the BiLST outperformed the SLT models for 3 categories. |
Dynamic Position Encoding for Transformers (2022.coling-1)
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| Challenge: | In neural machine translation, the general task of translating is to reduce the input sentence into smaller units (also known as statistical phrases), select an optimal translation for each unit, and place them in the correct order. |
| Approach: | They propose a novel architecture that relies on a feed-forward backbone and self-attention mechanism to encode sequential/positional information. |
| Outcome: | The proposed architecture improves on multiple datasets in French, Italian, and German and shows that it is more efficient than the current model. |
PAEG: Phrase-level Adversarial Example Generation for Neural Machine Translation (2022.coling-1)
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| Challenge: | Existing methods for adversarial example generation are word-level or character-level, which ignore the ubiquitous phrase structure. |
| Approach: | They propose a phrase-level adversarial example generation framework to enhance the robustness of the translation model by adopting a sentence-level substitution strategy. |
| Outcome: | The proposed method improves translation performance and robustness to noise on three benchmarks. |
Noise-robust Cross-modal Interactive Learning with Text2Image Mask for Multi-modal Neural Machine Translation (2022.coling-1)
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| Challenge: | Existing studies on multi-modal neural machine translation focus on visual information, but text and image may not match exactly, and visual noise is often ignored. |
| Approach: | They propose a noise-robust multi-modal interactive fusion approach with cross-modal relation-aware mask mechanism for MNMT. |
| Outcome: | The proposed model achieves state-of-the-art scores in all En-De, En-Fr and En-Cs translation tasks. |
Speeding up Transformer Decoding via an Attention Refinement Network (2022.coling-1)
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| Challenge: | Extensive experiments on ten WMT machine translation tasks show that the proposed model yields an average of 1.35x faster (with almost no decrease in BLEU) |
| Approach: | They propose a weighted residual network which reconstructs attention by reusing the features across layers. |
| Outcome: | The proposed model is 1.35x faster than the state-of-the-art inference model on translation tasks compared to AAN and SAN models with fewer parameter numbers . |
Interactive Post-Editing for Verbosity Controlled Translation (2022.coling-1)
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| Challenge: | Recent machine translation models have shown to excel with aspects of translation quality like adequacy and fluency but these models still suffer notable shortcomings like out-of-domain data, low-resource languages, rare words and longer sentences. |
| Approach: | They propose to use human-in-loop interactive post-editing models to improve translation quality and rephrase the text with a desired style variation. |
| Outcome: | The proposed model achieves BERTScore over state-of-the-art machine translation models while maintaining the desired token-level and verbosity preference. |
Addressing Asymmetry in Multilingual Neural Machine Translation with Fuzzy Task Clustering (2022.coling-1)
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| Challenge: | Existing clustering methods cannot handle asymmetric problem in multilingual NMT . existing models cannot handle the asymmetry problem since there are thousands of languages involved . |
| Approach: | They propose a fuzzy task clustering method to address the asymmetric problem in multilingual NMT by using task affinity as the clustering criterion. |
| Outcome: | The proposed method outperforms baselines for a multilingual model and the existing models. |
Learning Decoupled Retrieval Representation for Nearest Neighbour Neural Machine Translation (2022.coling-1)
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| Challenge: | Existing methods to integrate external corpus are sparse in practical applications, and noises in low similarity retrieval could lead to severe performance degradation. |
| Approach: | They propose a method to integrate external corpus into k-nearest neighbor machine translation (kNNMT) instead of storing discrete word sequence, kNN-MT uses a pre-trained NMT model to force decoding the external corpi. |
| Outcome: | The proposed approach improves retrieval accuracy and BLEU score on five domains compared to vanilla kNNMT. |
Semantically Consistent Data Augmentation for Neural Machine Translation via Conditional Masked Language Model (2022.coling-1)
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| Challenge: | Neural network models have a large number of parameters to train, but data augmentation is relatively under-explored in natural language processing. |
| Approach: | They propose a bi-directional conditional Masked Language Model (CMLM) that can be conditional on both left and right contexts and the label. |
| Outcome: | The proposed method achieves the best performance on four translation datasets and yields up to 1.90 BLEU points over the baseline. |
Informative Language Representation Learning for Massively Multilingual Neural Machine Translation (2022.coling-1)
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| Challenge: | Existing studies show that prepending language tokens fail to guide translation into right directions, especially on zero-shot translation. |
| Approach: | They propose to use language embedding embodiment and language-aware multi-head attention to learn informative language representations to channel translation into right directions. |
| Outcome: | The proposed methods improve translation direction guidance and significantly alleviate off-target translation issues on two datasets. |
Rare but Severe Neural Machine Translation Errors Induced by Minimal Deletion: An Empirical Study on Chinese and English (2022.coling-1)
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| Challenge: | Recent work examines MT pathologies on classification problems, the worst being hallucinations. |
| Approach: | They propose a method to induce severe translation errors by deleting a single character . they also analyze the effect of training data size on the number and types of errors induced by these perturbations . |
| Outcome: | The proposed method induces severe errors in Chinese-English and English-Chinese translations. |
QUAK: A Synthetic Quality Estimation Dataset for Korean-English Neural Machine Translation (2022.coling-1)
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| Challenge: | despite its high utility, there are limitations concerning manual QE data creation. |
| Approach: | They propose to generate a Korean-English QE dataset that is fully automatic . they find that the algorithm is more accurate and faster than manual QE . |
| Outcome: | The proposed datasets show that they scale up to 1.58M and 6.58M, respectively, and show that the results are significantly better when compared to the previous datasets. |
Improving Both Domain Robustness and Domain Adaptability in Machine Translation (2022.coling-1)
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| Challenge: | Existing approaches to domain adaptation for NMT depend on high-quality parallel data. |
| Approach: | They propose a meta-learning framework which improves domain robustness and adaptability . they use a word-level domain mixing model and a domain classifier to integrate it . |
| Outcome: | The proposed approach improves domain robustness and adaptability in seen and unseen domains. |
CoDoNMT: Modeling Cohesion Devices for Document-Level Neural Machine Translation (2022.coling-1)
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| Challenge: | Existing approaches to document-level neural machine translation focus on integrating context into translation, but they focus on the way of integrating contextual information into translation. |
| Approach: | They propose a document-level neural machine translation framework that models cohesion devices from two perspectives: Cohesion Device Masking and Cohetion Attention Focusing. |
| Outcome: | The proposed model outperforms state-of-the-art document-level neural machine translation baselines on three benchmark datasets. |
Improving Non-Autoregressive Neural Machine Translation via Modeling Localness (2022.coling-1)
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| Challenge: | Existing non-autoregressive neural machine translation models suffer from poor localization quality due to sequential dependencies within the target sentence. |
| Approach: | They propose to introduce local information into NAT models by explicitly introducing local information about surrounding words into the encoder and decoder sides to achieve localness-aware representations. |
| Outcome: | The proposed method can achieve significant improvements over strong NAT baselines. |
Categorizing Semantic Representations for Neural Machine Translation (2022.coling-1)
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| Challenge: | Modern neural machine translation models suffer limitation in compositional generalization, resulting in weakened translation performance on unseen compounds. |
| Approach: | They propose to introduce categorization to the contextualized representations to improve generalization by reducing sparsity and overfitting. |
| Outcome: | The proposed method reduces compositional generalization error rates by 24% on a dedicated MT dataset. |
Adversarial Training on Disentangling Meaning and Language Representations for Unsupervised Quality Estimation (2022.coling-1)
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| Challenge: | Existing methods for unsupervised quality estimation of machine translation are limited to several major language pairs. |
| Approach: | They propose a method to distill language-agnostic meaning embeddings from multilingual sentence encoders for unsupervised quality estimation of machine translation. |
| Outcome: | The proposed method achieves higher correlations with human evaluations on unsupervised translation quality estimation. |
Alleviating the Inequality of Attention Heads for Neural Machine Translation (2022.coling-1)
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| Challenge: | Recent studies show that the attention heads in Transformer are not equal. |
| Approach: | They propose a masking method to mask attention heads in Transformer . they empirically validate the inequality and propose 'head mask' method to avoid bottleneck . |
| Outcome: | The proposed masking method improves translation performance on multiple languages . it can be used to remove a small subset of heads without affecting performance . |
Adapting to Non-Centered Languages for Zero-shot Multilingual Translation (2022.coling-1)
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| Challenge: | Existing studies attributed zero-shot translation to domination of central language, e.g. English, but we supplement this viewpoint with the strict dependence of non-centered languages. |
| Approach: | They propose a language-specific modeling method that adapts to non-centered languages to counteract the instability of zero-shot translation. |
| Outcome: | The proposed method performs better than baselines in centered data conditions and can easily fit non-centered data. |
Towards Robust Neural Machine Translation with Iterative Scheduled Data-Switch Training (2022.coling-1)
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Zhongjian Miao, Xiang Li, Liyan Kang, Wen Zhang, Chulun Zhou, Yidong Chen, Bin Wang, Min Zhang, Jinsong Su
| Challenge: | Existing methods on robust neural machine translation (NMT) construct adversarial examples by injecting noise into authentic examples and indiscriminately exploit two types of examples. |
| Approach: | They propose an iterative scheduled data-switch training framework to mitigate this problem by injecting noise into authentic examples and indiscriminately exploiting two types of examples. |
| Outcome: | The proposed model outperforms several competitive benchmarks on four translation benchmarks. |
Cross-lingual Feature Extraction from Monolingual Corpora for Low-resource Unsupervised Bilingual Lexicon Induction (2022.coling-1)
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| Challenge: | Unsupervised bilingual lexicon induction models fail on low-resource language pairs due to insufficient initialization. |
| Approach: | They propose a method to learn cross-lingual features from monolingual corpora for low-resource UBLI by integrating cross-linguistic representations with pre-trained word embeddings in a fully unsupervised initialization. |
| Outcome: | The proposed method outperforms state-of-the-art methods on low-resource language pairs and improves representational ability and robustness of existing embedding models. |
Language-Independent Approach for Morphological Disambiguation (2022.coling-1)
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| Challenge: | Existing approaches for predicting complex morphological tags treat each analysis as a tag and apply sequence labeling models to perform tagging. |
| Approach: | They propose a language-independent approach which integrates all words, roots, POS and morpheme tags into vectors and computes the inner products between analyses and the contexts. |
| Outcome: | The proposed approach outperforms existing models on seven different languages while running about 6 and 33 times faster than MarMot and Seq2Seq, respectively. |
SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers (2022.coling-1)
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Bowen Qin, Lihan Wang, Binyuan Hui, Bowen Li, Xiangpeng Wei, Binhua Li, Fei Huang, Luo Si, Min Yang, Yongbin Li
| Challenge: | Existing methods that learn from multiple semantically-equivalent questions are limited to one-to-one mapping . |
| Approach: | They propose a constraint to explore the underlying complementary semantic information among multiple semantically-equivalent questions and learn robust feature representations with reduced spurious associations. |
| Outcome: | The proposed method outperforms strong competitors and achieves state-of-the-art results on five benchmark datasets. |
Deciphering and Characterizing Out-of-Vocabulary Words for Morphologically Rich Languages (2022.coling-1)
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| Challenge: | a detailed empirical case study of out-of-vocabulary words in modern text is presented . unfamiliar words cause trouble for machine processing or comprehension of text, authors say . |
| Approach: | They propose a detailed empirical case study of the nature of out-of-vocabulary words encountered in modern text in a moderate-resource language such as Bulgarian . they apply a multi-faceted distributional analysis of the underlying word-formation processes to characterize the residual vocabulary . |
| Outcome: | The proposed method can be used to aid in compositional translation, parsing, language modeling, and other NLP tasks. |
PSSAT: A Perturbed Semantic Structure Awareness Transferring Method for Perturbation-Robust Slot Filling (2022.coling-1)
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Guanting Dong, Daichi Guo, Liwen Wang, Xuefeng Li, Zechen Wang, Chen Zeng, Keqing He, Jinzheng Zhao, Hao Lei, Xinyue Cui, Yi Huang, Junlan Feng, Weiran Xu
| Challenge: | Existing slot filling models memorize inherent patterns of entities and contexts from training data. |
| Approach: | They propose a perturbed semantic structure awareness transferring method for slot filling models . they use two MLM-based training strategies to learn contextual semantic structure and word distribution . |
| Outcome: | The proposed method outperforms existing methods and gains strong generalization while preventing model from memorizing inherent patterns of entities and contexts. |
String Editing Based Chinese Grammatical Error Diagnosis (2022.coling-1)
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| Challenge: | Chinese Grammatical Error Diagnosis (CGED) suffers from numerous types of grammatical errors and insufficiency of training data. |
| Approach: | They propose a string editing based CGED model that uses a unified workflow to handle various types of grammatical errors. |
| Outcome: | The proposed model outperforms existing models on Chinese datasets in many aspects. |
The Fragility of Multi-Treebank Parsing Evaluation (2022.coling-1)
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| Challenge: | a limitation in NLP evaluation lies in the association between solving a dataset versus solving . authors often run their models only in a handful of treebanks . |
| Approach: | They propose to run a large-scale experiment on a single treebank and compare them on many parsers whose scores are available. |
| Outcome: | The proposed model can be biased on a single treebank and spurious effects can be avoided. |
FactMix: Using a Few Labeled In-domain Examples to Generalize to Cross-domain Named Entity Recognition (2022.coling-1)
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| Challenge: | Existing approaches for few-shot Named Entity Recognition (NER) are evaluated mainly under in-domain settings, but little is known about how these models perform in cross-domain NER using labeled in- domain examples. |
| Approach: | They propose to use a rationale-centric data augmentation method to improve model generalization ability by allowing model to learn from a few labeled examples in a new target domain. |
| Outcome: | The proposed method improves the performance of cross-domain NER tasks compared to the counterfactual data augmentation and prompt-tuning methods. |
Speaker-Aware Discourse Parsing on Multi-Party Dialogues (2022.coling-1)
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| Challenge: | Discourse parsing on multi-party dialogues is an important but difficult task in dialogue systems and conversational analysis. |
| Approach: | They propose a speaker-aware model for parsing on multi-party dialogues using interaction features between different speakers. |
| Outcome: | The proposed model achieves the best-reported performance on two standard benchmark datasets. |
Iterative Span Selection: Self-Emergence of Resolving Orders in Semantic Role Labeling (2022.coling-1)
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| Challenge: | Semantic role labeling is the task of labeling semantic arguments for marked semantic predicates. |
| Approach: | They propose a model which combines global decoding and iterative identification for the semantic arguments to consider their roles and relations in the labeling order. |
| Outcome: | The proposed model outperforms existing models in the benchmark datasets of span-based SRL: CoNLL-2005 and CoNll-2012. |
Revisiting the Practical Effectiveness of Constituency Parse Extraction from Pre-trained Language Models (2022.coling-1)
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| Challenge: | Constituency Parse Extraction from Pre-trained Language Models (CPE-PLM) is a new paradigm that attempts to induce constituency parse trees based on the internal knowledge of pre-tried language models. |
| Approach: | They propose to use constituency parse trees from pre-trained language models to induce constituency trees by introducing a set of heterogeneous PLMs combined using two advanced ensemble methods. |
| Outcome: | The proposed approach is more effective than typical supervised parsers in few-shot settings. |
Position Offset Label Prediction for Grammatical Error Correction (2022.coling-1)
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| Challenge: | Experimental results show that our proposed POL-Pc framework improves baseline models and yields consistent performance gain over various data augmentation methods. |
| Approach: | They propose a position offset label prediction subtask to integrate correction editing operations into a unified framework. |
| Outcome: | The proposed model outperforms baseline models on Chinese, English and Japanese datasets by a wide margin. |
Parsing Natural Language into Propositional and First-Order Logic with Dual Reinforcement Learning (2022.coling-1)
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Xuantao Lu, Jingping Liu, Zhouhong Gu, Hanwen Tong, Chenhao Xie, Junyang Huang, Yanghua Xiao, Wenguang Wang
| Challenge: | Existing methods to parse natural language into structured logical expressions have limitations due to paucity of labeled data. |
| Approach: | They propose a scoring model to automatically learn a model-based reward . they also propose introducing a Chinese-PL/FOL dataset to compensate for paucity of labeled data . |
| Outcome: | The proposed model outperforms competitors on several datasets. |
Yet Another Format of Universal Dependencies for Korean (2022.coling-1)
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Yige Chen, Eunkyul Leah Jo, Yundong Yao, KyungTae Lim, Miikka Silfverberg, Francis M. Tyers, Jungyeul Park
| Challenge: | Existing dependency parsers for Korean do not perform as well as their English counterparts due to the complexity of Korean's linguistic features. |
| Approach: | They propose a morpheme-based Korean dependency parsing format and propose to adopt it to Universal Dependencies. |
| Outcome: | The proposed format outperforms parsing results for Korean UD treebanks and detailed error analysis. |
Enhancing Structure-aware Encoder with Extremely Limited Data for Graph-based Dependency Parsing (2022.coling-1)
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| Challenge: | Dependency parsing is an important natural language processing task which analyzes the syntactic structure of an input sentence. |
| Approach: | They propose a structure-aware encoder pre-trained on auto-parsed data to improve dependency parsing . they propose combining gold dependency trees with existing parsers to improve parser performance . |
| Outcome: | The proposed approach outperforms baselines under different parsers and dependency standards under different parameters and model architectures. |
Simple and Effective Graph-to-Graph Annotation Conversion (2022.coling-1)
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| Challenge: | Existing work on graph-structured annotation conversions has focused on feature-based models which are not easily applicable to new conversions. |
| Approach: | They propose two graph-to-graph conversion approaches which use pseudo data and inherit parameters to guide conversions respectively. |
| Outcome: | The proposed approaches outperform strong baselines with higher conversion score on a graph-structured dataset and other datasets. |
BiBL: AMR Parsing and Generation with Bidirectional Bayesian Learning (2022.coling-1)
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| Challenge: | Existing approaches to AMR focus on one-side improvements despite the duality of the two tasks . instead, we propose data-efficient Bidirectional Bayesian learning (BiBL) to facilitate bidirectional information transition. |
| Approach: | They propose a data-efficient bidirectional Bayesian learning approach to facilitate bidirectional information transition by adopting a single-stage multitasking strategy. |
| Outcome: | The proposed model outperforms existing models on benchmark datasets without extra training data. |
Multi-Layer Pseudo-Siamese Biaffine Model for Dependency Parsing (2022.coling-1)
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| Challenge: | Existing work only uses biaffine method at the end of the dependency parser as a scorer, and its application in multi-layer form is ignored. |
| Approach: | They propose a multi-layer pseudo-Siamese biaffine model for neural dependency parsing that uses biaffin method as a scorer and a biaffin module to construct arc weight matrix. |
| Outcome: | The proposed model achieves state-of-the-art on PTB, CTB, and UD datasets with low efficiency loss. |
Belief Revision Based Caption Re-ranker with Visual Semantic Information (2022.coling-1)
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| Challenge: | Xu et al., 2015; You e t al, 2016) aimed at generating a natural language description for a given image. |
| Approach: | They propose a visual re-ranking approach that leverages visual-semantic measures to identify the ideal caption that maximally captures the visual information in the image. |
| Outcome: | The proposed approach improves the performance of image caption generation systems without training or fine-tuning. |
Towards Understanding the Relation between Gestures and Language (2022.coling-1)
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| Challenge: | a new study explores the relationship between gestures and language . we use contrastive learning to learn gesture embeddings . |
| Approach: | They adapt a semi-supervised multimodal model to learn gesture embeddings using Ted talks . they show gestures are predictive of the native language of the speaker . |
| Outcome: | The proposed model learns gesture embeddings from a multimodal dataset . it shows that gesture embeds are predictive of the native language of the speaker . |
Building Joint Relationship Attention Network for Image-Text Generation (2022.coling-1)
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| Challenge: | et al., 2017) focus on visual features individually, while ignoring relationship information among image features that provides important guidance for generating sentences. |
| Approach: | They propose a joint relationship attention network that explores the relationships among image features. |
| Outcome: | The proposed method achieves state-of-the-art performance on large-scale datasets and on Flickr30k datasets. |
Learning to Focus on the Foreground for Temporal Sentence Grounding (2022.coling-1)
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| Challenge: | Existing methods for temporal sentence grounding do not capture subtle details of small objects. |
| Approach: | They propose a detection-free framework for temporal sentence grounding that learns to locate foreground regions related to the query in consecutive frames. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on three challenging datasets. |
Are Visual-Linguistic Models Commonsense Knowledge Bases? (2022.coling-1)
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| Challenge: | PTLMs are used to extract knowledge from text on demand. |
| Approach: | They compare visual-linguistic and language-only visual-language models in a zero-shot commonsense question answering inference task. |
| Outcome: | The proposed models are highly promising on certain types of commonsense knowledge associated with the visual world. |
Visual Prompt Tuning for Few-Shot Text Classification (2022.coling-1)
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| Challenge: | Existing work on pretraining models for text classification uses image encoders instead of visual prompts. |
| Approach: | They propose a method to deploy large-scale pre-trained models in the prompt-tuning paradigm in few-shot learning. |
| Outcome: | The proposed method outperforms the most recent prompt-tuning methods on five public text classification datasets. |
Systematic Analysis of Image Schemas in Natural Language through Explainable Multilingual Neural Language Processing (2022.coling-1)
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| Challenge: | Existing methods for automatic detection of image schemas in natural language rely on specific assumptions about word classes as indicators of spatio-temporal events. |
| Approach: | They propose to train a supervised classifier that classifies natural language expressions into image schemas using a large dataset of examples from image schema literature. |
| Outcome: | The proposed model performs best in German, Russian, and French, and is based on a small dataset of examples from image schema literature. |
How to Adapt Pre-trained Vision-and-Language Models to a Text-only Input? (2022.coling-1)
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| Challenge: | Current language models have been criticised for learning language from text alone without connection between words and their meaning. |
| Approach: | They propose to train models on more sources than text to provide the lacking connection between words and their meanings. |
| Outcome: | The proposed model adaptation methods perform differently for different models and unimodal model counterparts perform on par with the VL models regardless of adaptation. |
ACT-Thor: A Controlled Benchmark for Embodied Action Understanding in Simulated Environments (2022.coling-1)
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| Challenge: | embodied AI tasks require a strong understanding of verbs and their corresponding actions. |
| Approach: | They propose a controlled benchmark for embodied action understanding using a simulated environment and a visual feature extractor. |
| Outcome: | The proposed benchmark achieves 81.4% accuracy and high inter-annotator agreement . the proposed model falls behind human models in a zero-shot scenario . |
In-the-Wild Video Question Answering (2022.coling-1)
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| Challenge: | Existing video understanding datasets focus on human interactions with little attention being paid to the “in the wild” settings. |
| Approach: | They propose a video understanding dataset of videos recorded outdoors . they propose identifying visual support for a given question and answer . |
| Outcome: | The proposed dataset examines the ability of models to understand videos, including video question answering, video captioning, and fill-inthe-blank tasks. |
Towards Better Semantic Understanding of Mobile Interfaces (2022.coling-1)
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Srinivas Sunkara, Maria Wang, Lijuan Liu, Gilles Baechler, Yu-Chung Hsiao, Jindong Chen, Abhanshu Sharma, James W. W. Stout
| Challenge: | a dataset of 500k unique annotations is released to improve mobile accessibility and automation capabilities. |
| Approach: | They propose to use an annotation dataset to improve the accessibility of mobile UIs . they use images and view hierarchies to augment annotations for icons and their semantics - and use multimodal inputs to build models. |
| Outcome: | The proposed dataset shows that it can be used to improve UIs and categories on unseen apps. |
End-to-end Dense Video Captioning as Sequence Generation (2022.coling-1)
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| Challenge: | Existing methods for dense video captioning use a two-stage generative process . but, more complex tasks are not able to fully utilize this powerful paradigm . |
| Approach: | They propose to model two subtasks of dense video captioning as one sequence generation task and predict the events and the corresponding descriptions. |
| Outcome: | Experiments on YouCook2 and ViTT show that the proposed model can be used on any video platform. |
SANCL: Multimodal Review Helpfulness Prediction with Selective Attention and Natural Contrastive Learning (2022.coling-1)
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| Challenge: | e-commerce has become a research hotspot for review helpfulness prediction . a new approach to help predict helpfulness of multimodal product reviews is proposed . |
| Approach: | They propose a machine learning task to identify helpfulness of multimodal product reviews . they use a probe-based strategy to enforce high attention weights on regions of greater significance . |
| Outcome: | The proposed model achieves state-of-the-art performance with lower memory consumption on two benchmark datasets with three categories. |
Dual Capsule Attention Mask Network with Mutual Learning for Visual Question Answering (2022.coling-1)
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| Challenge: | Visual Question Answering (VQA) models extract features from images and questions independently, but these methods fail to capture fine-grained key features and include much unnecessary information. |
| Approach: | They propose a dual capsule attention mask network with mutual learning for visual question answering (VQA) it contains two branches processing coarse-grained features and fine-grain features, respectively. |
| Outcome: | The proposed model outperforms baselines in terms of performance and interpretability and achieves new SOTA performance on the VQA-v2 dataset. |
Emergence of Hierarchical Reference Systems in Multi-agent Communication (2022.coling-1)
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| Challenge: | a hierarchical reference system allows the selection of the most appropriate level of specificity for a given context. |
| Approach: | They propose a hierarchical reference game to study the emergence of hierarchic reference systems in artificial agents. |
| Outcome: | The proposed game shows that agents can generalize to new concepts . the hierarchical reference game is based on a simplified world . |
Scene Graph Modification as Incremental Structure Expanding (2022.coling-1)
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| Challenge: | Scene graphs are used in cross-modal tasks such as image retrieval, image captioning, and visual question answering. |
| Approach: | They propose a model that iterates between nodes prediction and edges prediction . they frame scene graph modification as a graph expansion task by introducing incremental structure expanding . |
| Outcome: | The proposed model surpasses the state-of-the-art model by large margins on four benchmarks. |
Overcoming Language Priors in Visual Question Answering via Distinguishing Superficially Similar Instances (2022.coling-1)
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| Challenge: | Existing VQA models rely on the superficial correlation between question type and frequent answers to make predictions, without really understanding the input. |
| Approach: | They propose a training framework that explicitly encourages the VQA model to distinguish between superficially similar instances. |
| Outcome: | The proposed framework achieves state-of-the-art performance on VQA-CP v2 . it explicitly encourages the model to distinguish between the superficially similar instances . |
Efficient Multilingual Multi-modal Pre-training through Triple Contrastive Loss (2022.coling-1)
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| Challenge: | Existing approaches to learn visual and textual representations from web-scale image-text pairs are limited due to labeling cost and limited scalability. |
| Approach: | They propose to use web-scale image-text pairs to learn visual and textual representations in the shared space. |
| Outcome: | The proposed enhancement scheme improves multilingual vision-and-language tasks by minimizing a triplet contrastive loss on images and two different language texts with the same meaning. |
LOViS: Learning Orientation and Visual Signals for Vision and Language Navigation (2022.coling-1)
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| Challenge: | Existing Transformer-based VLN agents entangle orientation and vision information, which limits the learning of each information source. |
| Approach: | They propose to design a navigation agent with explicit Orientation and Vision modules . they use a set of pre-training tasks to feed the modules into the model . |
| Outcome: | The proposed model improves on R2R and R4R datasets and achieves state-of-the-art results. |
GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text Generation (2022.coling-1)
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| Challenge: | Recent improvements in KG-to-text generation are due to additional pre-training tasks . these tasks require extensive computational resources while only suggesting marginal improvements. |
| Approach: | They propose a mask structure to capture neighborhood information and a type encoder that adds a bias to the graph-attention weights depending on the connection type. |
| Outcome: | The proposed model outperforms state-of-the-art models while requiring no additional pre-training tasks. |
Content Type Profiling of Data-to-Text Generation Datasets (2022.coling-1)
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| Challenge: | Data-to-Text Generation (D2T) problems can be seen as a stream of time-stamped events with a textual summary of each event presenting the insights. |
| Approach: | They propose a typology of content types to classify the contents of event summaries using a dataset as the distribution of the aggregated content types. |
| Outcome: | The proposed typology shows that neural systems struggle in generating complex types on different datasets. |
CoLo: A Contrastive Learning Based Re-ranking Framework for One-Stage Summarization (2022.coling-1)
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| Challenge: | Existing methods for extractive and abstractive summarization use token-level or sentence-level training objectives. |
| Approach: | They propose a Contrastive Learning based re-ranking framework for one-stage summarization called CoLo. |
| Outcome: | The proposed framework boosts extractive and abstractive results on CNN/DailyMail benchmarks while maintaining inference efficiency. |
Of Human Criteria and Automatic Metrics: A Benchmark of the Evaluation of Story Generation (2022.coling-1)
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| Challenge: | Existing studies on automatic story generation (ASG) rely on human criteria, but there is little research on how well they correlate with human criteria. |
| Approach: | They propose to use human criteria to evaluate automatic story generation (ASG) their paper proposes to use HANNA to quantitatively evaluate correlations between 72 automatic metrics and human criteria. |
| Outcome: | The proposed model compared human criteria with automatic criteria and found that they were significantly better than human criteria. |
Selective Token Generation for Few-shot Natural Language Generation (2022.coling-1)
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| Challenge: | Experimental results show that the proposed selective token generation algorithm outperforms the previous additive learning algorithms based on the PLMs. |
| Approach: | They propose an additive learning algorithm that selectively outputs language tokens between a task-general PLM and a specific adapter during training and inference. |
| Outcome: | The proposed algorithm outperforms existing methods on few-shot natural language generation tasks. |
A-TIP: Attribute-aware Text Infilling via Pre-trained Language Model (2022.coling-1)
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| Challenge: | Existing methods for text infilling focus on the infill length of blanks and attribute relevance, but attribute-aware content can be more useful. |
| Approach: | They propose an attribute-aware text infilling method via a Pre-trained language model which contains a text in filling component and a plug-and-play discriminator. |
| Outcome: | The proposed method improves attribute relevance without decreasing text fluency on three open-source datasets. |
Multi Graph Neural Network for Extractive Long Document Summarization (2022.coling-1)
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| Challenge: | Heterogeneous Graph Neural Networks (GNN) have been proposed as an emergent approach for extracting document summarization (EDS) but there are still limitations in applying it for long documents due to the lack of inter-sentence connections. |
| Approach: | They propose to build a graph on sentence-level nodes and combine it with HeterGNN to capture the semantic information in terms of both inter and intra-sentence connections. |
| Outcome: | Experiments on two datasets show that the proposed method achieves state-of-the-art in this research field. |
Improving Zero-Shot Multilingual Text Generation via Iterative Distillation (2022.coling-1)
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| Challenge: | Existing approaches to generalize multilingual dialogue systems to multilingual settings often make assumptions about data availability. |
| Approach: | They propose to transfer inductive biases for target languages learned by pretrained teacher models to student models via sequence-level knowledge distillation. |
| Outcome: | The proposed method performs well on the multiATIS++ benchmark, and is comparable to human annotations in both slot F1 and intent accuracy. |
Using Structured Content Plans for Fine-grained Syntactic Control in Pretrained Language Model Generation (2022.coling-1)
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| Challenge: | Large pretrained language models can generate powerful text but cannot be controlled at a sub-sentential level. |
| Approach: | They propose to make such fine-grained control possible in pretrained LMs by generating text directly from a semantic representation, Abstract Meaning Representation (BART), which is augmented at the node level with syntactic control tags. |
| Outcome: | The proposed method can generate text from a semantic representation, which is augmented at the node level with syntactic control tags. |
PrefScore: Pairwise Preference Learning for Reference-free Summarization Quality Assessment (2022.coling-1)
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| Challenge: | Existing studies on summarization evaluation without a human-written reference summary have shown high correlations with human ratings. |
| Approach: | They propose to judge summary quality by learning preference rank from corrupted summaries . they use Bradley-Terry power ranking model to learn preference rank . |
| Outcome: | Experiments on several datasets show that the proposed model can produce scores highly correlated with human ratings. |
Multi-Attribute Controlled Text Generation with Contrastive-Generator and External-Discriminator (2022.coling-1)
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| Challenge: | Existing studies on controlled text generation focus on single-attribute control, but in practical applications, they lack controllability. |
| Approach: | They propose a framework for multi-attribute controlled text generation that can effectively generate texts with more attributes. |
| Outcome: | The proposed framework achieves remarkable controllability while keeping the text fluent and diverse. |
Coordination Generation via Synchronized Text-Infilling (2022.coling-1)
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| Challenge: | Generating synthetic data from pre-trained language models has enhanced performance across several NLP tasks. |
| Approach: | They propose a method for generating sentences with a coordinate structure in which the boundaries of its conjuncts are explicitly specified. |
| Outcome: | The proposed method produces promising coordination instances that provide gains for the task in low-resource settings. |
KHANQ: A Dataset for Generating Deep Questions in Education (2022.coling-1)
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| Challenge: | Existing QG datasets are not suitable for educational question generation because the questions are not real questions asked by humans during learning. |
| Approach: | They propose a dataset for question generation that contains 1,034 high-quality learner-generated questions seeking an in-depth understanding of the taught online courses in Khan Academy. |
| Outcome: | The proposed dataset contains 1,034 high-quality learner-generated questions seeking an in-depth understanding of the taught online courses in Khan Academy. |
Multi-Figurative Language Generation (2022.coling-1)
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| Challenge: | Figurative language generation is the task of reformulating a given text in the desired figure of speech while still being faithful to the original context. |
| Approach: | They propose a scheme for multi-figurative language pre-training on top of BART and a mechanism for injecting the target figurative information into the encoder to generate text with the target figure from another figurativ form without parallel figura-figura pairs. |
| Outcome: | The proposed model outperforms all baselines and qualitatively examines the relationship between the different figures of speech. |
Enhancing Task-Specific Distillation in Small Data Regimes through Language Generation (2022.coling-1)
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| Challenge: | Large-scale pretrained language models have led to significant improvements in Natural Language Processing, but they come at the cost of high computational and storage requirements. |
| Approach: | They propose to distill knowledge from larger models to smaller ones through pseudo-labels on task-specific datasets. |
| Outcome: | The proposed approach improves on the SST-2, MRPC, YELP-2, and TREC-6 datasets. |
Boosting Code Summarization by Embedding Code Structures (2022.coling-1)
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| Challenge: | Recent work on code summarization relies on structural information from the abstract syntax tree (AST) of source codes. |
| Approach: | They propose a program dependency graph (PDG) that represents the structure of a code more effectively. |
| Outcome: | The proposed model improves the performance of an out-of-domain benchmark dataset and the measure SBERT score. |
Comparative Graph-based Summarization of Scientific Papers Guided by Comparative Citations (2022.coling-1)
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| Challenge: | Comparative citations can help researchers find related and comparable articles and common topics. |
| Approach: | They propose a comparative graph-based summarization method to find related articles and compare them using citations as guidance. |
| Outcome: | The proposed method outperforms baselines on CSSC and performs well on DUC2006 and DUC2007. |
JPG - Jointly Learn to Align: Automated Disease Prediction and Radiology Report Generation (2022.coling-1)
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| Challenge: | Existing methods rarely consider cross-modal alignment between textual and visual features and ignore disease tags as auxiliary for report generation. |
| Approach: | They propose a "Jointly learning framework for automated disease Prediction and radiology report Generation" the framework integrates cross-modal alignment between textual and visual features and disease tags to improve the quality of reports. |
| Outcome: | The proposed framework improves the quality of radiology reports by combining the main task and auxiliary tasks. |
Automatic Nominalization of Clauses through Textual Entailment (2022.coling-1)
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| Challenge: | Past research on clause nominalization has focused on replacement of the head verb with a deverbal noun and resource development to support the task. |
| Approach: | They propose to use a textual entailment model to optimize the position and POS of nominal arguments by fine-tuning a model on the task. |
| Outcome: | The proposed model outperforms unsupervised approaches on the nominalization task and outperformed a state-of-the-art neural language model. |
Benchmarking Compositionality with Formal Languages (2022.coling-1)
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| Challenge: | Compositionality is a hallmark of human language, but it is not yet fully understood . recombining known primitive concepts into larger novel combinations is elusive . |
| Approach: | They use finite-state transducers to make a dataset with controllable compositionality . they find that the models either learn the relations completely or not at all . |
| Outcome: | The proposed model learns the relation completely or not at all on large datasets. |
Source-summary Entity Aggregation in Abstractive Summarization (2022.coling-1)
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| Challenge: | Existing studies on the semantics of text generated by abstractive summarization systems have focused on summary n-grams that are not found in the source text. |
| Approach: | They study how entities from a source text can be referred to in later discourse by a more general description. |
| Outcome: | The proposed method shows that state-of-the-art summarization systems produce semantically correct aggregations. |
How to Find Strong Summary Coherence Measures? A Toolbox and a Comparative Study for Summary Coherence Measure Evaluation (2022.coling-1)
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| Challenge: | Existing methods to evaluate summary coherence are often evaluated using disparate datasets and metrics. |
| Approach: | They propose to use automatic evaluation to evaluate coherence of summaries by selecting high-scoring candidates. |
| Outcome: | The proposed methods show that they can perform better on an even playing field. |
Summarizing Dialogues with Negative Cues (2022.coling-1)
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| Challenge: | Abstractive dialogue summarization aims to convert long dialogue content into its short form where the salient information is preserved while the redundant pieces are ignored. |
| Approach: | They propose to have the model perceive the redundant parts of an input dialogue history during the training phase. |
| Outcome: | The proposed method significantly outperforms baselines on the semantic matching and factual consistent based metrics. |
ALEXSIS-PT: A New Resource for Portuguese Lexical Simplification (2022.coling-1)
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| Challenge: | Lexical simplification (LS) is the task of replacing complex words with simpler alternatives to make texts more accessible to various target populations. |
| Approach: | They propose to use a Brazilian Portuguese multi-candidate dataset to test LS systems. |
| Outcome: | The proposed model outperforms existing models on Brazilian Portuguese and Brazilian newspaper articles. |
APPDIA: A Discourse-aware Transformer-based Style Transfer Model for Offensive Social Media Conversations (2022.coling-1)
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| Challenge: | Using style-transfer models to reduce offensiveness of social media comments is difficult because of limited labeled data. |
| Approach: | They propose two methods to integrate discourse relations with pretrained style-transfer models and evaluate them on a reddit dataset. |
| Outcome: | The proposed models can reduce offensiveness while preserving original meaning . they are the first to examine inferential links between comment and original text . |
View Dialogue in 2D: A Two-stream Model in Time-speaker Perspective for Dialogue Summarization and beyond (2022.coling-1)
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| Challenge: | Existing models for dialogue summarization focus on document summarizing on time and speaker-centered points, but this approach is limited in understanding the dialogue. |
| Approach: | They propose a 2D view of dialogue based on a time-speaker perspective where the time and speaker streams of dialogue can be obtained as strengthened input. |
| Outcome: | The proposed model outperforms existing models on the QMSum dataset and improves summary faithfulness and human evaluation. |
Denoising Large-Scale Image Captioning from Alt-text Data Using Content Selection Models (2022.coling-1)
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| Challenge: | Recent approaches to training large-scale image captioning (IC) models often fall short in terms of performance in leveraging noisy datasets in favor of clean annotations. |
| Approach: | They propose a technique that breaks down the task into two smaller, more controllable tasks - skeleton prediction and skelet-based caption generation. |
| Outcome: | The proposed method can generate better and denoised captions when using noisy datasets. |
Meta-CQG: A Meta-Learning Framework for Complex Question Generation over Knowledge Bases (2022.coling-1)
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| Challenge: | Existing methods train one encoder-decoder-based model to fit all questions . however, such a one-size-fits-all strategy may not perform well for complex questions involving multiple KB relations or functional constraints. |
| Approach: | They propose a meta-learning framework for complex question generation over knowledge bases . they propose he meta-trained generator can acquire universal meta-knowledge . |
| Outcome: | The proposed framework can acquire universal and transferable meta-knowledge and quickly adapt to long-tailed samples under different dimensions. |
Graph-to-Text Generation with Dynamic Structure Pruning (2022.coling-1)
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| Challenge: | Recent studies show that explicitly modeling the input graph structure can significantly improve the performance. |
| Approach: | They propose a structure-aware cross-attention mechanism to re-encode the graph representation conditioning on the newly generated context at each decoding step. |
| Outcome: | The proposed model improves performance on two graph-to-text datasets with only minor increase on computational cost. |
Multi-Perspective Document Revision (2022.coling-1)
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| Challenge: | a novel document revision task that revises multiple perspectives is proposed . grammatical error correction tasks have been studied in the natural language processing field . |
| Approach: | They propose a Japanese multi-perspective document revision task that revises multiple perspectives to improve the readability and clarity of a document. |
| Outcome: | The proposed model can be used to improve the readability and clarity of a document. |
A Survey of Automatic Text Summarization Using Graph Neural Networks (2022.coling-1)
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| Challenge: | Abstractive ATS involves generating factually correct and fluent sentences. |
| Approach: | They provide an overview of the use of graph neural networks (GNNs) for automatic text summarization. |
| Outcome: | The proposed model is based on a set of graph neural networks (GNNs) that are used to generate a concise, correct and fluent summary of a given text. |
Phrase-Level Localization of Inconsistency Errors in Summarization by Weak Supervision (2022.coling-1)
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| Challenge: | Existing methods for evaluating inconsistency in summarization are limited . a recent study found that more than 30% of summarized summaries are inconsistent with the source documents . |
| Approach: | They propose a method for localizing inconsistency errors in summarization using a synthetic dataset that contains factual errors likely to be produced by a common language processor. |
| Outcome: | The proposed method detects factual errors more accurately than existing weakly supervised methods . the proposed model also detects errors in original sentences more accurately . |
PoliSe: Reinforcing Politeness Using User Sentiment for Customer Care Response Generation (2022.coling-1)
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| Challenge: | Human-machine interactions have increased rapidly assisting humans in their everyday lives. |
| Approach: | They propose to automatically identify the sentiment of the user and transform the neutral responses into polite responses conforming to the sentiment and the conversational history. |
| Outcome: | The proposed approach achieves superior performance compared to baseline models. |
Focus-Driven Contrastive Learning for Medical Question Summarization (2022.coling-1)
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| Challenge: | Existing methods to summarize health questions are not able to capture well question focus and lack the ability to understand sentence-level semantics. |
| Approach: | They propose a question focus-driven contrastive learning framework to capture question focus and exploit contrastive training at both encoder and decoder to obtain better sentence representations. |
| Outcome: | The proposed model achieves 5.33, 12.85 and 3.81 points over the baseline model on three medical benchmark datasets. |
ArgLegalSumm: Improving Abstractive Summarization of Legal Documents with Argument Mining (2022.coling-1)
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| Challenge: | Existing abstractive summarization models do not take into account argumentative structure of legal documents, which poses a challenge towards effective abstractive summary. |
| Approach: | They propose a technique that integrates argument role labeling into the summarization process by integrating argument role labels into the document. |
| Outcome: | The proposed method improves over strong baselines with pretrained language models. |
Semantic Overlap Summarization among Multiple Alternative Narratives: An Exploratory Study (2022.coling-1)
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| Challenge: | Existing tasks for summarizing multiple alternate narratives with different perspectives are under-explored. |
| Approach: | They propose a task which entails generating a single summary from multiple alternative narratives . they use a web-based dataset and human annotations to evaluate the task . |
| Outcome: | The proposed task is based on a novel dataset and human annotations. |
Analyzing the Dialect Diversity in Multi-document Summaries (2022.coling-1)
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| Challenge: | Social media posts provide compelling, yet challenging source of data of diverse perspectives . automatic text summarization algorithms preserve salient information from the source text . |
| Approach: | They propose to compress large collections of documents into short summaries that preserve salient information from the source text. |
| Outcome: | The proposed dataset shows that humans annotate fairly well-balanced dialect diverse summaries without loss of diversity. |
Multi-Document Scientific Summarization from a Knowledge Graph-Centric View (2022.coling-1)
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| Challenge: | Multi-Document Scientific Summarization (MDSS) aims to produce concise and concise summaries for clusters of topic-relevant scientific papers. |
| Approach: | They propose a model that incorporates knowledge graphs into paper encoding and decoding processes and propose 'decoder' for generating knowledge graph information of summary in the form of descriptive sentences. |
| Outcome: | The proposed architecture improves on baselines on the Multi-Xscience dataset. |
Generation of Patient After-Visit Summaries to Support Physicians (2022.coling-1)
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Pengshan Cai, Fei Liu, Adarsha Bajracharya, Joe Sills, Alok Kapoor, Weisong Liu, Dan Berlowitz, David Levy, Richeek Pradhan, Hong Yu
| Challenge: | After-visit summary is a summary note given to patients after their clinical visit. |
| Approach: | They propose to automate the generation of after-visit summaries and introduce a feedback mechanism that alerts physicians when an automatic summary fails to capture important details of the clinical notes. |
| Outcome: | The proposed system improves on a large clinical dataset that contains electronic health record (EHR) notes and their associated summaries. |
HeterGraphLongSum: Heterogeneous Graph Neural Network with Passage Aggregation for Extractive Long Document Summarization (2022.coling-1)
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| Challenge: | Existing models for extractive document summarization are based on sequence-to-sequence (Seq2Sequency) but long-form document summaries using graph-based methods are still an open research issue. |
| Approach: | They propose a heterogeneous graph neural network model to improve the performance of extractive document summarization using graph-based methods. |
| Outcome: | The proposed model can achieve state-of-the-art performance without pre-trained language models. |
GRETEL: Graph Contrastive Topic Enhanced Language Model for Long Document Extractive Summarization (2022.coling-1)
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| Challenge: | Existing approaches to capture and integrate global semantic information are limited due to their limited ability to capture long-range dependencies. |
| Approach: | They propose a graph contrastive topic enhanced language model that integrates a neural topic model with a pre-trained language model to capture global contextual semantics. |
| Outcome: | The proposed model outperforms existing methods on general domain and biomedical datasets. |
PINEAPPLE: Personifying INanimate Entities by Acquiring Parallel Personification Data for Learning Enhanced Generation (2022.coling-1)
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Sedrick Scott Keh, Kevin Lu, Varun Gangal, Steven Y. Feng, Harsh Jhamtani, Malihe Alikhani, Eduard Hovy
| Challenge: | Personifications are figures of speech that endow inanimate entities with properties and actions typically seen as requiring animacy. |
| Approach: | They propose to use personification data to train a parallel corpus of personifications . they propose to combine personification-related literalizations with automatic ones . |
| Outcome: | The proposed personification system can generate diverse and creative personifications . it can generate personification-related qualities such as interestingness and animacy . |
Mind the Gap! Injecting Commonsense Knowledge for Abstractive Dialogue Summarization (2022.coling-1)
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| Challenge: | Existing frameworks that use commonsense as supervision only use input knowledge, but it generates more informative and consistent summaries. |
| Approach: | They propose to leverage the unique characteristics of dialogues sharing commonsense knowledge to solve the difficulties in summarizing them. |
| Outcome: | The proposed framework generates more informative and consistent summaries with injected commonsense knowledge than existing methods. |
Type-dependent Prompt CycleQAG : Cycle Consistency for Multi-hop Question Generation (2022.coling-1)
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| Challenge: | Existing research on multi-hop question generation (QG) has not been done due to its complexity. |
| Approach: | They propose a type-dependent prompt cycleQAG with a cycle consistency loss . they propose to use the question type and words related to the correct answer as prompts . |
| Outcome: | The proposed model outperforms the baseline model by 10.38% based on ROUGE score. |
UPER: Boosting Multi-Document Summarization with an Unsupervised Prompt-based Extractor (2022.coling-1)
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| Challenge: | Multi-Document Summarization (MDS) uses the extract-then-abstract paradigm, which extracts a relatively short meta-document and then feeds it into the deep neural networks to generate an abstract. |
| Approach: | They propose to use pre-trained language models to calculate document and keyword’s perplexity to boost other metrics for evaluating a document’s salience. |
| Outcome: | The proposed method can be applied as a plug-in to boost other metrics for evaluating a document’s salience, thus improving the subsequent abstract generation. |
DISK: Domain-constrained Instance Sketch for Math Word Problem Generation (2022.coling-1)
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| Challenge: | Existing methods for generating MWP text from equations are inflexible and require pre-defined templates. |
| Approach: | They propose a neural model which generates MWPs from equations by constructing a Quantity Cell Graph from the retrieved MWp instance and reasoning over it. |
| Outcome: | The proposed model performs impressively on educational MWP set and on human evaluation metrics. |
Context-Tuning: Learning Contextualized Prompts for Natural Language Generation (2022.coling-1)
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| Challenge: | Recent studies have shown that pretrained language models (PLMs) lack sufficient consideration of input semantics to generate natural language. |
| Approach: | They propose a continuous prompting approach to fine-tune PLMs for natural language generation by modeling an inverse generation process from output to input. |
| Outcome: | The proposed method fine-tunes only 0.12% of the parameters while maintaining good performance. |
PSP: Pre-trained Soft Prompts for Few-Shot Abstractive Summarization (2022.coling-1)
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| Challenge: | Experimental results show that our method outperforms full-model tuning in few-shot abstractive summarization tasks. |
| Approach: | They propose a soft prompts architecture with prompt pre-training and prompt fine-tuning paradigm to support few-shot abstractive summarization. |
| Outcome: | The proposed model outperforms Prompt Tuning and Profix-Tuning on CNN/DailyMail and XSum datasets and outperfies Profix Tuning by a large margin. |
Continuous Decomposition of Granularity for Neural Paraphrase Generation (2022.coling-1)
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| Challenge: | Prior work has shown that decomposing sentences at different levels of granularity has improved paragraph generation. |
| Approach: | They propose a model for continuous decomposing granularity for neural paraphrase generation that incorporates granules into attention. |
| Outcome: | The proposed model outperforms baseline models on Quora question pairs and Twitter URLs on two benchmarks. |
Paraphrase Generation as Unsupervised Machine Translation (2022.coling-1)
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| Challenge: | Existing methods for paraphrase generation rely on labeled datasets or are limited in narrow domains. |
| Approach: | They propose a paradigm for paraphrase generation by treating the task as unsupervised machine translation based on pairs of unlabeled monolingual sentences. |
| Outcome: | The proposed paradigm can generate paraphrases on a large unlabeled monolingual corpus without relying on bilingual sentence pairs. |
Summarize, Outline, and Elaborate: Long-Text Generation via Hierarchical Supervision from Extractive Summaries (2022.coling-1)
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| Challenge: | Existing models focus on local word prediction, and cannot make high level plans on what to generate. |
| Approach: | They propose a pipelined system that summarises, outlines and elaborates on each bullet point to generate the corresponding segment. |
| Outcome: | The proposed system produces long texts with significantly better quality and faster convergence speed. |
CoCGAN: Contrastive Learning for Adversarial Category Text Generation (2022.coling-1)
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| Challenge: | Experimental results on synthetic and real category text generation datasets demonstrate that CoCGAN can achieve significant improvements over the baseline category text generators. |
| Approach: | They propose to incorporate contrastive learning into adversarial category text generation by using a discriminator to optimize a contrastive learn objective to capture more flexible data-to-class relations and data- to-data relations among training samples. |
| Outcome: | The proposed model improves on synthetic and real category text generation datasets. |
An Efficient Coarse-to-Fine Facet-Aware Unsupervised Summarization Framework Based on Semantic Blocks (2022.coling-1)
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| Challenge: | Existing unsupervised summarization methods fail to consider efficiency and effectiveness when the input document is extremely long. |
| Approach: | They propose an efficient Coarse-to-Fine Facet-Aware Ranking framework for unsupervised long document summarization based on the semantic block. |
| Outcome: | The proposed framework can achieve new state-of-the-art unsupervised summarization results on Gov-Report, billSum, arXiv, and PubMed. |
CHAE: Fine-Grained Controllable Story Generation with Characters, Actions and Emotions (2022.coling-1)
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| Challenge: | Existing studies on story generation focus on coarse-grained control of the story, neglecting the details of the narrative. |
| Approach: | They propose a model for fine-grained control on the story that allows the generation of customized stories with characters, corresponding actions and emotions arbitrarily assigned. |
| Outcome: | The proposed method has strong controllability to generate customized stories according to the fine-grained personalized guidance. |
Chinese Couplet Generation with Syntactic Information (2022.coling-1)
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| Challenge: | Chinese couplet generation aims to generate a pair of clauses with certain rules adhered . previous studies have focused on learning the correspondences between antecedent and subsequent clauses . |
| Approach: | They propose to leverage syntactic information to generate Chinese couplets by POS tags and word dependencies. |
| Outcome: | The proposed approach outperforms baselines on a Chinese couplet generation dataset. |
Noise-injected Consistency Training and Entropy-constrained Pseudo Labeling for Semi-supervised Extractive Summarization (2022.coling-1)
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| Challenge: | Existing studies on semi-supervised learning methods focus on how to effectively utilize abundant unlabeled data. |
| Approach: | They propose a semi-supervised consistency training method to regularize model predictions and a pseudo-labeling strategy to obtain high-confidence labels from unlabeled predictions. |
| Outcome: | The proposed method improves extractive summarization over an insufficient labeled dataset. |
Question Generation Based on Grammar Knowledge and Fine-grained Classification (2022.coling-1)
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| Challenge: | Recent research on question generation has achieved great success, but some question types and answers did not match. |
| Approach: | They construct a question type classifier and a query generator to solve the problem of question types not matching with other questions. |
| Outcome: | The proposed model improves the accuracy of interrogative words in generated questions. |
CM-Gen: A Neural Framework for Chinese Metaphor Generation with Explicit Context Modelling (2022.coling-1)
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| Challenge: | Nominal metaphors are commonly used in human language and have been shown to be effective in persuading, expressing emotion, and stimulating interest. |
| Approach: | They propose a multitask framework which optimizes three tasks: NM identification, NM component identification, and NM generation. |
| Outcome: | The proposed framework outperforms baselines on consistency and creativity on the NM generation task in Chinese. |
Psychology-guided Controllable Story Generation (2022.coling-1)
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| Challenge: | Existing controllable story generation systems ignore the psychological changes of the protagonists and focus on the appointed keywords or emotions. |
| Approach: | They propose a Psychology-guided Controllable Story Generation System (PICS) that generates stories that adhere to the given leading context and desired psychological state chains for the protagonist. |
| Outcome: | The proposed system outperforms baselines and shows that it can generate stories with more consistent psychological changes. |
Few-shot Table-to-text Generation with Prefix-Controlled Generator (2022.coling-1)
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| Challenge: | Neural table-to-text generation approaches are data-hungry and lack labeled data. |
| Approach: | They propose a prompt-based approach for few-shot table-to-text generation using a task-specific prefix and an input-specific input prefix. |
| Outcome: | The proposed approach is able to generate table-to-text summaries with a few instances and is validated on human, book and song datasets. |
Text Simplification of College Admissions Instructions: A Professionally Simplified and Verified Corpus (2022.coling-1)
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| Challenge: | a dataset of 112 admissions instructions is used to simplify the language used by higher education institutions to communicate with prospective students. |
| Approach: | They propose to simplify admissions instructions by professionally simplifying them and comparing them to a dataset of 112 admissions documents. |
| Outcome: | The proposed dataset includes 112 admissions instructions from higher education institutions across the US. |
On the Role of Pre-trained Language Models in Word Ordering: A Case Study with BART (2022.coling-1)
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| Challenge: | Existing work uses linear models and neural networks for word ordering, yet pre-trained language models have not been studied in word ordering. |
| Approach: | They propose a constrained language generation task using unordered words as input. |
| Outcome: | The proposed model is able to perform better than existing models and proves to be reliable. |
Visual Information Guided Zero-Shot Paraphrase Generation (2022.coling-1)
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| Challenge: | Several studies use different information as ”pivot” such as language, semantic representation and so on. |
| Approach: | They propose to use visual information as the "pivot" of back-translation to generate paraphrases using paired image-caption data. |
| Outcome: | The proposed model generates paraphrase with good relevancy, fluency and diversity . it is based on paired image-caption data and can train a paraphrasing model . |
Improving Abstractive Dialogue Summarization with Speaker-Aware Supervised Contrastive Learning (2022.coling-1)
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| Challenge: | Existing summarization systems based on pre-trained models cannot recognize the unique format of the speaker-utterance pair well in the dialogue. |
| Approach: | They propose three speaker-aware supervised contrastive learning tasks to solve the speaker identification problem in dialogue summarization task. |
| Outcome: | The proposed methods improve on two mainstream dialogue summarization datasets. |
Diversifying Neural Text Generation with Part-of-Speech Guided Softmax and Sampling (2022.coling-1)
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| Challenge: | Existing methods to generate text using contextual features do not consider syntactic structure clues. |
| Approach: | They propose using linguistic annotation, i.e., part-of-speech (POS), to guide the text generation. |
| Outcome: | The proposed method can generate more diverse text while maintaining comparable quality. |
Enhancing Pre-trained Models with Text Structure Knowledge for Question Generation (2022.coling-1)
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| Challenge: | Existing question generation models treat input passage as a sequence-to-sequence generative task, but they are not aware of text structure. |
| Approach: | They propose to model text structure as answer position and syntactic dependency and propose a mask attention mechanism to make syntaktic structure of input passage accessible. |
| Outcome: | The proposed model outperforms the strong pre-trained model ProphetNet on a SQuAD dataset and achieves competitive results with the state-of-the-art model. |
LFKQG: A Controlled Generation Framework with Local Fine-tuning for Question Generation over Knowledge Bases (2022.coling-1)
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| Challenge: | Existing KBQG models focus on the most relevant part of the answer entity, while neglecting the rest of the subgraph. |
| Approach: | They propose a controlled generation framework for Question Generation over Knowledge Bases that generates questions with out-of-vocabulary (OOV) predicates. |
| Outcome: | The proposed framework outperforms existing methods significantly on three widely-used benchmark datasets SimpleQuestion, PathQuestions, and WebQuestIONS. |
Demystifying Neural Fake News via Linguistic Feature-Based Interpretation (2022.coling-1)
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| Challenge: | Recent advances to neural fake news generators have made it difficult to understand how misinformation generated by these models may best be confronted. |
| Approach: | They conduct feature-based analysis to gain an interpretative understanding of the linguistic attributes that neural fake news generators may most effectively exploit. |
| Outcome: | The proposed models are compared with models trained on subsets of features and confronted with increasingly advanced neural fake news. |
Measuring Geographic Performance Disparities of Offensive Language Classifiers (2022.coling-1)
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| Challenge: | Recent work shows that text classifiers are biased regarding different languages and dialects. |
| Approach: | They propose to use a dataset to examine whether language, dialect, and topical content vary across geographical regions to address these gaps. |
| Outcome: | The proposed dataset includes 14 thousand examples across 15 cities and shows that current models do not generalize across locations. |
Offensive Content Detection via Synthetic Code-Switched Text (2022.coling-1)
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| Challenge: | Existing methods to detect offensive content in social media platforms are limited by the availability of labeled code-switched data. |
| Approach: | They propose a method for generating synthetic code-switched offensive content data using human-generated data and a keyword classification baseline. |
| Outcome: | The proposed algorithm can be used to generate synthetic code-switched offensive content data and train it on human-generated data. |
A Survey on Multimodal Disinformation Detection (2022.coling-1)
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Firoj Alam, Stefano Cresci, Tanmoy Chakraborty, Fabrizio Silvestri, Dimiter Dimitrov, Giovanni Da San Martino, Shaden Shaar, Hamed Firooz, Preslav Nakov
| Challenge: | Recent years have witnessed the proliferation of offensive content online such as fake news, propaganda, misinformation, and disinformation. |
| Approach: | They propose to tackle online multimodal offensive content using different modalities and combinations thereof. |
| Outcome: | The proposed approach combines factuality and harmfulness in a framework that can be used for multiple modalities and combinations of modality. |
Why Is It Hate Speech? Masked Rationale Prediction for Explainable Hate Speech Detection (2022.coling-1)
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| Challenge: | Hate speech cannot be identified based solely on the presence of specific words; model should reason like humans and be explainable. |
| Approach: | They propose to use Masked Rationale Prediction to predict masked human rationales . the method performs hate speech detection robustly in terms of bias and explainability . |
| Outcome: | The proposed method performs state-of-the-art in terms of bias and explainability. |
Domain Classification-based Source-specific Term Penalization for Domain Adaptation in Hate-speech Detection (2022.coling-1)
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| Challenge: | Existing approaches for hate-speech detection exhibit poor performance in out-of-domain settings due to overemphasizing source-specific information that negatively impacts its domain invariance. |
| Approach: | They propose a domain adaptation approach that automatically extracts and penalizes source-specific terms using a classifier. |
| Outcome: | The proposed approach improves cross-domain evaluation on indomain held-out instances while preserving high performance on out-of-domain settings. |
Generalizable Implicit Hate Speech Detection Using Contrastive Learning (2022.coling-1)
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| Challenge: | Hate speech detection is challenging when there are insufficient lexical cues. |
| Approach: | They propose a contrastive learning method that pulls an implication and its corresponding posts close in representation space. |
| Outcome: | The proposed method improves on BERT and HateBERT benchmarks on three implicit hate speech benchmarks. |
Social Bot-Aware Graph Neural Network for Early Rumor Detection (2022.coling-1)
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| Challenge: | Existing models do not distinguish genuine users from social bots, and their failure in identifying rumors timely. |
| Approach: | They propose to account for social bots’ behavior and construct a Social Bot-Aware Graph Neural Network to model early propagation of posts and then use it to detect rumors. |
| Outcome: | The proposed method achieves significant improvements over baselines and identifies rumors within 3 hours while maintaining more than 90% accuracy. |
A Contrastive Cross-Channel Data Augmentation Framework for Aspect-Based Sentiment Analysis (2022.coling-1)
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| Challenge: | Aspect-based sentiment analysis is sensitive to multi-aspect challenges, resulting in multiple aspects in a sentence. |
| Approach: | They propose a framework that leverages an in-domain generator to construct more multi-aspect samples . they then boost the robustness of ABSA models via contrastive learning on these generated samples ." |
| Outcome: | The proposed framework outperforms baselines without any augmentations on accuracy and Macro- F1 . the proposed framework can generate more multi-aspect samples and boost the robustness of ABSA models . |
Sentiment Interpretable Logic Tensor Network for Aspect-Term Sentiment Analysis (2022.coling-1)
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| Challenge: | Aspect-term sentiment analysis (ATSA) is a fine-grained task that aims to infer the sentiment towards the given aspect-terms. |
| Approach: | They propose a novel ATSA method that is interpretable and has high accuracy . they propose SILTN, which is a neurosymbolic formalism, to improve the accuracy based on syntax knowledge distillation. |
| Outcome: | The proposed method is interpretable because it is a neurosymbolic formalism and a computational model that supports learning and reasoning about data with a differentiable first-order logic language. |
Detecting Minority Arguments for Mutual Understanding: A Moderation Tool for the Online Climate Change Debate (2022.coling-1)
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| Challenge: | polarized topics such as climate change present challenges for moderators and researchers. |
| Approach: | They propose a moderation tool to support moderators in promoting mutual understanding in the online climate change debate by training classifiers to label incoming posts for the arguments they entail and using active learning to supplement the training data with rare arguments. |
| Outcome: | The proposed method can be part of the toolkit for moderators struggling with polarized topics such as climate change. |
A Multi-turn Machine Reading Comprehension Framework with Rethink Mechanism for Emotion-Cause Pair Extraction (2022.coling-1)
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| Challenge: | Emotion-cause pair extraction (ECPE) is an emerging task in emotion cause analysis, which extracts potential emotion-caused pairs from an emotional document. |
| Approach: | They propose a document-level machine reading comprehension task to model complex relations between emotions and causes while avoiding generating the pairing matrix. |
| Outcome: | The proposed framework outperforms existing state-of-the-art methods on the emotion cause corpus and can model complex relations between emotions and causes while avoiding pairing matrix. |
Structural Bias for Aspect Sentiment Triplet Extraction (2022.coling-1)
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| Challenge: | Existing structural bias adapters for aspect sentiment triplet extraction are under-confident . a large-scale dataset for ASTE shows the adapter is effective and efficient to a larger scale. |
| Approach: | They propose to use a structural adapter to integrate structural bias into pretrained language models . they propose to add a relative position structure in place of the syntactic dependency structure . |
| Outcome: | The proposed adapter achieves state-of-the-art performance over strong baselines, but with a light parameter demand and low latency. |
Unsupervised Data Augmentation for Aspect Based Sentiment Analysis (2022.coling-1)
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| Challenge: | Recent approaches to Aspect-based Sentiment Analysis (ABSA) perform the subtasks of aspect term extraction (ATE) and aspect sentiment classification (ASC) simultaneously. |
| Approach: | They introduce an adaptation of Unsupervised Data Augmentation in semi-supervised learning that performs both aspects of Aspect-based Sentiment Analysis (ABSA) and aspect sentiment classification (ASC) they show that simple augmentations applied to modest-sized datasets along with consistency training lead to competitive performance with current ABSA state-of-the-art in restaurant and laptop domains . |
| Outcome: | The proposed approach performs well on a span-level classification task with minimal training data. |
A Sentiment and Emotion Aware Multimodal Multiparty Humor Recognition in Multilingual Conversational Setting (2022.coling-1)
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| Challenge: | Humor is an essential aspect of daily conversation, and people try to provoke humor in their talks. |
| Approach: | They propose a multitask framework that annotates Hindi utterances with sentiment and emotion classes. |
| Outcome: | The proposed framework improves on the recently released Hindi Humor dataset . it takes sentiment and emotion into account to understand humor . |
TSAM: A Two-Stream Attention Model for Causal Emotion Entailment (2022.coling-1)
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| Challenge: | Existing studies on EAC focus on Emotion Recognition in Conversations (ERC), i.e., recognizing emotion labels of utterances. |
| Approach: | They propose a two-stream attention model to capture correlations between utterances in a global view and classify multiple utterrances synchronously to capture emotion and speaker information in parallel. |
| Outcome: | The proposed model outperforms baselines and achieves new State-Of-The-Art (SOTA) performance. |
Entity-Level Sentiment Analysis (ELSA): An Exploratory Task Survey (2022.coling-1)
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| Challenge: | Existing tasks and models for identifying sentiment expressed in text are lacking in identifying overall sentiment . prior work focused on document-level polarity classification, but ELSA is under-explored for longer texts with multiple mentions and opinions towards the same entity. |
| Approach: | They propose to use document-, sentence-, and target-level sentiment analysis to identify overall sentiment expressed towards volitional entities in a document. |
| Outcome: | The proposed task is referred to as "entity-level sentiment analysis" the proposed task performs poorly for longer texts with multiple mentions and opinions . |
Learning from Adjective-Noun Pairs: A Knowledge-enhanced Framework for Target-Oriented Multimodal Sentiment Classification (2022.coling-1)
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| Challenge: | Existing methods to determine sentiment polarity of opinion target are inconsistent and lack visual attention. |
| Approach: | They propose a framework which can exploit adjective-noun pairs extracted from images to improve visual attention and sentiment prediction capability of the TMSC task. |
| Outcome: | The proposed framework outperforms state-of-the-art on two public datasets. |
Towards Exploiting Sticker for Multimodal Sentiment Analysis in Social Media: A New Dataset and Baseline (2022.coling-1)
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| Challenge: | Sentiment analysis in social media is challenging because of the lack of context. |
| Approach: | They propose to use stickers to perform a multimodal sentiment analysis task using Chinese stickers. |
| Outcome: | The proposed model performs best compared with other models. |
Natural Language Inference Prompts for Zero-shot Emotion Classification in Text across Corpora (2022.coling-1)
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| Challenge: | Existing models for textual emotion classification depend on domain and application scenario and need to be predefined . a natural language inference model with a flexible set of labels is difficult to develop . |
| Approach: | They propose to use the paradigm of zero-shot learning as a natural language inference task to generate a model with a flexible set of labels. |
| Outcome: | The proposed model is more robust across corpora than individual prompts and shows similar performance to the best prompt for a particular corpus. |
CommunityLM: Probing Partisan Worldviews from Language Models (2022.coling-1)
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| Challenge: | Political polarization is accelerating as political discourse diverges linguistically . et al. ( 2017) show that partisanship makes reliable predictions about an individual's word understanding . |
| Approach: | They propose a framework that probes community-specific responses to a survey using community language models CommunityLM. |
| Outcome: | The proposed framework can query the worldview of any group of people given a sufficiently large sample of their social media discussions or media diet. |
Composition-based Heterogeneous Graph Multi-channel Attention Network for Multi-aspect Multi-sentiment Classification (2022.coling-1)
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| Challenge: | Existing methods for Aspect-based sentiment analysis (ABSA) focus on aspect terms with the same sentiment polarity . current methods focus on sentences with only one aspect term or multiple aspect terms . |
| Approach: | They propose a novel method to model inter-aspect relationships and aspect-context relationships simultaneously using a heterogeneous graph. |
| Outcome: | The proposed method can predict sentiments towards the given aspect term in a sentence . it can provide more detailed predictions compared with sentence-level sentiment analysis. |
CoNTACT: A Dutch COVID-19 Adapted BERT for Vaccine Hesitancy and Argumentation Detection (2022.coling-1)
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| Challenge: | CoNTACT is a Dutch language model adapted to the domain of COVID-19 tweets . a turbulent vaccine debate has emerged between advocates and opponents of vaccines - a polarization that will continue to influence future views on vaccines. |
| Approach: | They propose a Dutch language model adapted to the domain of COVID-19 tweets . they use 2.8M Dutch COVId-19 related tweets posted in 2021 to test the model . |
| Outcome: | The proposed model shows statistically significant gains over RobBERT on two tasks. |
SSR: Utilizing Simplified Stance Reasoning Process for Robust Stance Detection (2022.coling-1)
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| Challenge: | Existing methods for stance detection are task-agnostic, which fail to utilize task knowledge to better discriminate between genuine and bias features. |
| Approach: | They propose to incorporate stance reasoning process as task knowledge to aid in learning genuine features without using targets. |
| Outcome: | The proposed model achieves better performance than previous task-agnostic debiasing methods on new test sets. |
Transferring Confluent Knowledge to Argument Mining (2022.coling-1)
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| Challenge: | Argument mining is a natural language processing task that seeks to obtain structured arguments from unstructured text. |
| Approach: | They propose to use a transfer learning methodology to assess the potential of argument mining knowledge with confluent tasks. |
| Outcome: | The proposed method dispenses with heavy feature and model engineering and allows for new state-of-the-art performance for its three main sub-tasks. |
When to Laugh and How Hard? A Multimodal Approach to Detecting Humor and Its Intensity (2022.coling-1)
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| Challenge: | Existing methods to generate humor using multimodal data are needed to study the role of humor in human social function. |
| Approach: | They propose a model that automatically detects humor in the Friends TV show using multimodal data and use prerecorded laughter as annotation as it marks humor. |
| Outcome: | The proposed model detects humor 78% of the time and how long the audience’s laughter reaction should last with a mean absolute error of 600 milliseconds. |
Modeling Aspect Correlation for Aspect-based Sentiment Analysis via Recurrent Inverse Learning Guidance (2022.coling-1)
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| Challenge: | Existing methods to learn complex sentence with multiple aspects do not consider correlation between aspects to distinguish overlapped feature. |
| Approach: | They propose a method that uses aspect correlation to improve aspect correlation modeling . they use Recurrent Mechanism to improve the joint representation of aspects . |
| Outcome: | The proposed method is state-of-the-art in multiaspect scenarios. |
Analyzing Persuasion Strategies of Debaters on Social Media (2022.coling-1)
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| Challenge: | Existing studies on the analysis of persuasion in online discussions focus on the effectiveness of comments in individual discussions and ignore the effectiveness analysis of debaters over multiple discussions. |
| Approach: | They propose to quantify debaters effectiveness in the online discussion platform "ChangeMyView" they aim to explore diverse insights into their persuasion strategies . |
| Outcome: | The proposed analysis of debater effectiveness in the ChangeMyView subreddit reveals that debaters have different levels of effectiveness, behavioral characteristics and text stylistic features . |
KC-ISA: An Implicit Sentiment Analysis Model Combining Knowledge Enhancement and Context Features (2022.coling-1)
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| Challenge: | Existing research results on explicit sentiment analysis are limited . implicit sentiment analysis is a process of analyzing text based on whether it contains explicit sentiment words. |
| Approach: | They propose a model that integrates external knowledge and contextual features . they use a knowledge graph to supplement implicit sentiment expression . |
| Outcome: | The proposed model can achieve better results on the SMP2019 implicit sentiment analysis dataset. |
Domain Generalization for Text Classification with Memory-Based Supervised Contrastive Learning (2022.coling-1)
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| Challenge: | Existing approaches to cross-domain text classification focus on one-to-one domain adaptation. |
| Approach: | They propose a framework for domain generalization that uses contrastive learning with a memory-saving queue. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on Amazon review sentiment datasets and rumour detection datasets. |
A Zero-Shot Claim Detection Framework Using Question Answering (2022.coling-1)
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| Challenge: | Existing claims detection frameworks are portability to emerging events and low-resource training data settings. |
| Approach: | They propose a claim detection framework that leverages zero-shot Question Answering to solve sub-tasks such as topic filtering, claim object detection, and claimer detection. |
| Outcome: | The proposed framework outperforms baselines on the NewsClaims benchmark. |
Asymmetric Mutual Learning for Multi-source Unsupervised Sentiment Adaptation with Dynamic Feature Network (2022.coling-1)
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| Challenge: | Recent work on pre-trained language models (PrLMs) on labeled sentiment datasets has shown significant improvements on widerange of NLP tasks, including sentiment classification. |
| Approach: | They propose a multi-source unsupervised sentiment adaptation problem with pre-trained features to exploit the extracted pre-train features for efficient domain adaptation. |
| Outcome: | The proposed model outperforms the state-of-the-art methods on multiple sentiment benchmarks and extensive ablation studies to verify the effectiveness of each module. |
Target Really Matters: Target-aware Contrastive Learning and Consistency Regularization for Few-shot Stance Detection (2022.coling-1)
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| Challenge: | stance detection is a task to identify attitudes from opinions towards certain targets, but it is expensive and time-consuming . stance detector is based on labeled data, but unlabeled data can be collected easier . |
| Approach: | They propose a semi-supervised framework for few-shot stance detection that uses unlabeled data to learn more distinguishable representations for different targets. |
| Outcome: | The proposed framework achieves state-of-the-art performance on multiple benchmark datasets. |
Joint Alignment of Multi-Task Feature and Label Spaces for Emotion Cause Pair Extraction (2022.coling-1)
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| Challenge: | Existing methods for ECPE fail to model specific features and interactive features in between, or suffer from inconsistency of label prediction. |
| Approach: | They propose to align ECPE with a feature-task alignment mechanism to model emotion-&cause-specific features and the shared interactive feature. |
| Outcome: | The proposed model outperforms existing systems on all ECA subtasks. |
Causal Intervention Improves Implicit Sentiment Analysis (2022.coling-1)
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| Challenge: | Existing neural models struggle with implicit sentiment analysis because they latch onto spurious correlations, resulting in poor generalization and robustness. |
| Approach: | They propose a CausaL intervention model for implicit sEntiment ANalysis using instrumental variable to eliminate confounding causal effects and extract the pure causal effect between sentence and sentiment. |
| Outcome: | The proposed model extracts the pure causal effect between sentence and sentiment using instrumental variable. |
COMMA-DEER: COmmon-sense Aware Multimodal Multitask Approach for Detection of Emotion and Emotional Reasoning in Conversations (2022.coling-1)
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| Challenge: | Mental health is a critical component of the United Nations’ Sustainable Development Goals (SDGs), particularly Goal 3 which aims to provide “good health and well-being”. |
| Approach: | They propose a task of detecting emotional reasoning and accompanying emotions in conversations that is manually annotated at the utterance level. |
| Outcome: | The proposed model achieves 6% accuracy and 4.62% accuracy on the emotion detection task and 3.56% accuracy, and 3.31% F1 on the ER detection task, compared to the existing state-of-the-art model. |
EmoMent: An Emotion Annotated Mental Health Corpus from Two South Asian Countries (2022.coling-1)
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Thushari Atapattu, Mahen Herath, Charitha Elvitigala, Piyanjali de Zoysa, Kasun Gunawardana, Menasha Thilakaratne, Kasun de Zoysa, Katrina Falkner
| Challenge: | Recent research using AI and NLP demonstrates strong potential to automatically detect mental health issues from digital footprints such that professionals could provide timely interventions and mental health resources to vulnerable persons. |
| Approach: | They developed an emotion-annotated mental health corpus from 2802 Facebook posts extracted from two South Asian countries, Sri Lanka and India. |
| Outcome: | The proposed model achieved 98.3% agreement between the annotators and a Fleiss’ Kappa of 0.82. |
LEGO-ABSA: A Prompt-based Task Assemblable Unified Generative Framework for Multi-task Aspect-based Sentiment Analysis (2022.coling-1)
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| Challenge: | Existing generative methods focus on a single task at a time. |
| Approach: | They propose a unified generative multi-task framework that can solve multiple ABSA tasks . they propose to control the type of task prompts consisting of multiple element prompts . |
| Outcome: | The proposed framework achieves state-of-the-art results in almost all ABSA tasks and competitive results in task transfer scenarios. |
A Hierarchical Interactive Network for Joint Span-based Aspect-Sentiment Analysis (2022.coling-1)
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| Challenge: | Existing methods for aspect-sentiment analysis ignore internal correlations between aspect extraction and sentiment classification. |
| Approach: | They propose a hierarchical interactive network to model two-way interactions between two tasks appropriately using shallow-level and deep-level inputs. |
| Outcome: | Extensive experiments on three real-world datasets demonstrate that the proposed model outperforms existing methods. |
MuCDN: Mutual Conversational Detachment Network for Emotion Recognition in Multi-Party Conversations (2022.coling-1)
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| Challenge: | Emotion recognition in multi-party conversations is a challenging task that predicts the emotion for each utterance. |
| Approach: | They propose to separate conversations into detached threads to capture emotional clues in conversational context . they propose to use mutual detachment networks to perform context and speaker-specific modeling within detached thread. |
| Outcome: | The proposed model outperforms baseline models on two datasets. |
UECA-Prompt: Universal Prompt for Emotion Cause Analysis (2022.coling-1)
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| Challenge: | Existing methods adopt fine-tuning paradigm to solve certain types of ECA tasks. Existing models suffer from dataset bias. |
| Approach: | They propose a universal prompt tuning method to solve different ECA tasks in a unified framework and a sequential learning module to ease the dataset bias. |
| Outcome: | The proposed method achieves competitive performance on the ECA datasets. |
One-Teacher and Multiple-Student Knowledge Distillation on Sentiment Classification (2022.coling-1)
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| Challenge: | Existing knowledge distillation models require large computing resources and long inference time to perform. |
| Approach: | They propose a one-teacher and multiple-student knowledge distillation approach to distill a deep pre-trained teacher model into multiple shallow student models with ensemble learning. |
| Outcome: | The proposed method achieves better results with fewer parameters and extremely high speedup ratios on three sentiment classification tasks. |
Making Parameter-efficient Tuning More Efficient: A Unified Framework for Classification Tasks (2022.coling-1)
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Xin Zhou, Ruotian Ma, Yicheng Zou, Xuanting Chen, Tao Gui, Qi Zhang, Xuanjing Huang, Rui Xie, Wei Wu
| Challenge: | Large pre-trained language models (PLMs) have demonstrated superior performance in industrial applications. |
| Approach: | They propose a framework that re-uses existing parameter-efficient methods with a unified classifier. |
| Outcome: | The proposed framework improves the efficiency of existing parameter-efficient methods with a unified classifier. |
A Multi-Task Dual-Tree Network for Aspect Sentiment Triplet Extraction (2022.coling-1)
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| Challenge: | Existing methods are poor at detecting complicated relations between aspects and opinions . detecting unclear boundaries of multi-word aspects and opinion is also a challenge . |
| Approach: | They propose a multi-task dual-tree network to extract triplets from a given sentence . they employ a constituency tree and a modified dependency tree to enhance the interaction . |
| Outcome: | The proposed model extracts triplets from a given sentence, and it is effective on four datasets. |
Exploiting Unlabeled Data for Target-Oriented Opinion Words Extraction (2022.coling-1)
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Yidong Wang, Hao Wu, Ao Liu, Wenxin Hou, Zhen Wu, Jindong Wang, Takahiro Shinozaki, Manabu Okumura, Yue Zhang
| Challenge: | Existing methods to extract opinion words from sentences are limited due to the expensive annotation process. |
| Approach: | They propose to exploit massive unlabeled data to reduce distribution shift risk . they propose to use two filters specifically for TOWE to filter noisy data . results indicate superiority of MGCR over current state-of-the-art methods . |
| Outcome: | The proposed method reduces the risk of distribution shifts by increasing the exposure of the model to varying distribution shift. |
Learnable Dependency-based Double Graph Structure for Aspect-based Sentiment Analysis (2022.coling-1)
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| Challenge: | Existing methods for aspect-based sentiment classification are susceptible to dependency tree due to noisy information and neglecting rich relation information between words. |
| Approach: | They propose a dependency-based double graph model for aspect-based sentiment classification that incorporates structure, relations and linguistic features into the sentiment text. |
| Outcome: | The proposed model is superior to state-of-the-art methods on four benchmark datasets. |
A Structure-Aware Argument Encoder for Literature Discourse Analysis (2022.coling-1)
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Yinzi Li, Wei Chen, Zhongyu Wei, Yujun Huang, Chujun Wang, Siyuan Wang, Qi Zhang, Xuanjing Huang, Libo Wu
| Challenge: | Existing research for argument representation learning treats tokens in sentences equally and ignores the implied structure information of argumentative context. |
| Approach: | They propose to separate tokens into two groups to capture structural information of arguments and to incorporate paragraph-level position information into the model. |
| Outcome: | The proposed model captures structural information of arguments and is able to identify arguments automatically. |
Mere Contrastive Learning for Cross-Domain Sentiment Analysis (2022.coling-1)
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| Challenge: | Existing approaches to cross-domain sentiment analysis are labor-intensive and time-consuming. |
| Approach: | They propose a modified contrastive objective with in-batch negative samples to allow sentence representations from the same class to be pushed closer while those from the different classes become further apart in the latent space. |
| Outcome: | The proposed model can achieve state-of-the-art in cross-domain and multi-domain sentiment analysis tasks while transferring knowledge learned in the source domain to the target domain. |
Exploiting Sentiment and Common Sense for Zero-shot Stance Detection (2022.coling-1)
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| Challenge: | Existing stance detection models use sentiment and commonsense knowledge to classify stance toward documents and topics . obtaining rich annotated data in stance detector is time-consuming and laborintensive . |
| Approach: | They propose to use sentiment and commonsense knowledge to boost transferability of stance detection model by using sentiment and similar knowledge. |
| Outcome: | The proposed model outperforms the state-of-the-art methods on the zero-shot and few-shot benchmark datasets. |
Modeling Intra- and Inter-Modal Relations: Hierarchical Graph Contrastive Learning for Multimodal Sentiment Analysis (2022.coling-1)
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| Challenge: | Existing studies in Multimodal Sentiment Analysis lack a mechanism to understand complex relations between different modalities. |
| Approach: | They propose a hierarchical graph contrastive learning framework for multimodal sentiment analysis that explores the relationships between modality representations. |
| Outcome: | The proposed framework outperforms the state-of-the-art in multimodal sentiment analysis on two benchmark datasets. |
AMOA: Global Acoustic Feature Enhanced Modal-Order-Aware Network for Multimodal Sentiment Analysis (2022.coling-1)
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| Challenge: | Existing methods treat three modal features equally, without distinguishing the importance of different modalities. Existing models split the video into frames, leading to missing the global acoustic information. |
| Approach: | They propose a global Acoustic feature enhanced Modal-Order-Aware network to address these problems. |
| Outcome: | The proposed model outperforms state-of-the-art models on two public datasets. |
Keyphrase Prediction from Video Transcripts: New Dataset and Directions (2022.coling-1)
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Amir Pouran Ben Veyseh, Quan Hung Tran, Seunghyun Yoon, Varun Manjunatha, Hanieh Deilamsalehy, Rajiv Jain, Trung Bui, Walter W. Chang, Franck Dernoncourt, Thien Huu Nguyen
| Challenge: | Existing studies on keyphrase prediction have focused on formal texts and informal-text domains. |
| Approach: | They propose to annotate large-scale video transcripts with keyphrases from live-stream video . they propose to feed models with paragraph-level keyphrase extraction to foster future research . |
| Outcome: | The proposed model improves keyphrase prediction in live-stream video transcripts by feeding models with paragraph-level keyphrases. |
Event Extraction in Video Transcripts (2022.coling-1)
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| Challenge: | Existing EE datasets are limited to formally written documents such as news articles or scientific papers . existing EE methods and datasets cannot be used in informal and noisy texts . |
| Approach: | They propose to use video transcripts as a dataset for event extraction . they demonstrate that existing state-of-the-art EE methods cannot achieve adequate performance . |
| Outcome: | The proposed dataset evaluates state-of-the-art EE methods on streamed videos on Behance . it shows that such systems cannot achieve adequate performance on the proposed dataset . |
Recycle Your Wav2Vec2 Codebook: A Speech Perceiver for Keyword Spotting (2022.coling-1)
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| Challenge: | Pretraining a keyword Spotting model with a pretraining encoder is expensive and requires a quadratic cost. |
| Approach: | They propose to recycle phonetic information encoded in wav2vec2.0's latent codebook, which has been typically thrown away after pretraining. |
| Outcome: | The proposed model can be initialized with phonetic embeddings, and it delivers accuracy gains at no latency costs. |
Improving Code-switched ASR with Linguistic Information (2022.coling-1)
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| Challenge: | Existing studies on code-switching have been limited to the individual languages, but the results are promising. |
| Approach: | They propose to apply linguistic theories to generate more realistic code-switching text, which is needed for language modelling in ASR. |
| Outcome: | The proposed system improves 2% on English-Spanish code-switching . Equivalence Constraint theory and part-of-speech labelling are particularly helpful for text generation and bring 2% improvement to ASR performance. |
Language-specific Effects on Automatic Speech Recognition Errors for World Englishes (2022.coling-1)
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| Challenge: | Existing systems are not able to meet the needs of speakers of different demographic groups. |
| Approach: | They propose to analyze the performance of Otter’s automatic captioning system on native and non-native English speakers of different language background through a linguistic analysis of segment-level errors. |
| Outcome: | The proposed system predicts certain errors from the phonological structure of a speaker’s native language. |
A Transformer-based Threshold-Free Framework for Multi-Intent NLU (2022.coling-1)
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| Challenge: | Existing models for multi-intent natural language understanding mainly detect multiple intents on threshold settings. |
| Approach: | They propose a transformer-based multi-intent NLU model with multi-task learning that exploits the information of the number of multiple intents in each utterance without additional manual annotations. |
| Outcome: | The proposed model achieves superior results on two public multi-intent datasets. |
Unsupervised Multi-scale Expressive Speaking Style Modeling with Hierarchical Context Information for Audiobook Speech Synthesis (2022.coling-1)
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| Challenge: | a recent study has shown that expressiveness of audiobooks is limited by the averaged global-scale speaking style representation. |
| Approach: | They propose an unsupervised multi-scale context-sensitive text-to-speech model for audiobooks . they use hierarchical context encoder to predict global-scale contextual style embeddings . |
| Outcome: | The proposed model outperforms existing models on a real-world Mandarin audio dataset. |
Incorporating Instructional Prompts into a Unified Generative Framework for Joint Multiple Intent Detection and Slot Filling (2022.coling-1)
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| Challenge: | Existing approaches to multiple intent detection and slot filling focus on task-specific components to capture the relationships between intents and slots. |
| Approach: | They propose a Unified Generative framework that captures the relationships between intents and slots in an utterance and formulates the task as a question-answering problem. |
| Outcome: | The proposed framework surpasses baselines on full-data and multi-intent benchmarks on 5-shot and 10-shot scenarios. |
Adaptive Unsupervised Self-training for Disfluency Detection (2022.coling-1)
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| Challenge: | Recent studies on disfluency detection heavily relies on human annotations, which are difficult and expensive to obtain in practice. |
| Approach: | They propose an unsupervised method that reweights the importance of each training example according to its grammatical feature and prediction confidence. |
| Outcome: | The proposed method improves 2.3 points over the current SOTA unsupervised method and is competitive with the SOTA supervised method. |