Findings of the Association for Computational Linguistics: ACL 2022
“Is Whole Word Masking Always Better for Chinese BERT?”: Probing on Chinese Grammatical Error Correction (2022.findings-acl)
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| Challenge: | a Chinese model with whole word masking has no subword because each token is an atomic character. |
| Approach: | They propose to use whole word masking to mask all subwords corresponding to a word at once . they ask models to revise or insert tokens in a masked language modeling manner . |
| Outcome: | The proposed model performs better when one character is inserted or replaced . the model trained with standard character-level masking performs best when one token is masked . |
Compilable Neural Code Generation with Compiler Feedback (2022.findings-acl)
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Xin Wang, Yasheng Wang, Yao Wan, Fei Mi, Yitong Li, Pingyi Zhou, Jin Liu, Hao Wu, Xin Jiang, Qun Liu
| Challenge: | Existing deep-learning approaches model code generation as text generation, but few of them account for compilability of the generated programs. |
| Approach: | They propose a three-stage pipeline utilizing compiler feedback for compilable code generation to improve compilability. |
| Outcome: | The proposed pipeline improves compilability of generated programs by combining compiler feedback, language model fine-tuning, and compilable discrimination. |
Towards Unifying the Label Space for Aspect- and Sentence-based Sentiment Analysis (2022.findings-acl)
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| Challenge: | Existing methods to train ABSA model are limited by lack of annotated data . a dual-granularity pseudo labeling approach is proposed to solve this problem . |
| Approach: | They propose a framework for aspect-based sentiment analysis that uses annotated data to train ABSA models. |
| Outcome: | The proposed framework surpasses previous methods on benchmarks. |
Input-specific Attention Subnetworks for Adversarial Detection (2022.findings-acl)
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| Challenge: | a new method to prune attention heads is proposed for adversarial detection . attention heads in models such as BERT are over-provisioned and can be pruned . |
| Approach: | They propose a method to construct input-specific attention subnetworks from which three features are extracted to discriminate between authentic and adversarial inputs. |
| Outcome: | The proposed method significantly improves state-of-the-art adversarial detection accuracy on 10 NLU datasets with 11 different adversarials. |
RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction (2022.findings-acl)
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| Challenge: | Existing approaches to extract relation triplets require large datasets and a fixed set of relations. |
| Approach: | They propose to use a sentence-based task setting to generalize relation extraction methods to unseen relation sets. |
| Outcome: | The proposed method can extract multiple relation triplets in a sentence using language model prompts and structured text approaches. |
Pre-Trained Multilingual Sequence-to-Sequence Models: A Hope for Low-Resource Language Translation? (2022.findings-acl)
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En-Shiun Lee, Sarubi Thillainathan, Shravan Nayak, Surangika Ranathunga, David Adelani, Ruisi Su, Arya McCarthy
| Challenge: | Pre-trained multilingual sequence-to-sequence models like mBART and mT5 can be used to translate low-resource languages, but their practical application is unclear. |
| Approach: | They conduct an empirical experiment in 10 languages to determine what can pre-trained multilingual sequence-to-sequence models like mBART do to translate low-resource languages? |
| Outcome: | The proposed models are robust to domain differences, but translations for unseen and typologically distant languages remain below 3.0 BLEU. |
Multi-Scale Distribution Deep Variational Autoencoder for Explanation Generation (2022.findings-acl)
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| Challenge: | Existing methods for generating explanations for recommender systems produce generic explanations that fail to incorporate user and item specific details. |
| Approach: | They propose a multi-scale distribution deepvariational autoencoder with a prior network that eliminates noise while retaining meaningful signals in the input. |
| Outcome: | The proposed models can generate explanations with concrete input-specific contents. |
Dual Context-Guided Continuous Prompt Tuning for Few-Shot Learning (2022.findings-acl)
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| Challenge: | Existing prompt-based paradigms have shown their competitive performance in many NLP tasks, but their effectiveness varies upon the model and training data. |
| Approach: | They propose a dual context-guided continuous prompt tuning method that integrates contextual information into the input input. |
| Outcome: | The proposed method outperforms existing prompt tuning methods in the few-shot setting and can be used in many NLP tasks. |
Extract-Select: A Span Selection Framework for Nested Named Entity Recognition with Generative Adversarial Training (2022.findings-acl)
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| Challenge: | Existing studies treat named entity recognition as a sequential labeling problem. |
| Approach: | They propose a span selection framework for nested named entity recognition . they propose nesting entities with different input categories would be separately extracted . |
| Outcome: | The proposed framework outperforms competing models on four benchmark datasets. |
Controlled Text Generation Using Dictionary Prior in Variational Autoencoders (2022.findings-acl)
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| Challenge: | Variational autoencoders (VAEs) have been widely applied in text generation tasks, but they suffer from insufficient representation capacity and poor controllability. |
| Approach: | They propose a data-driven prior that has expressivity and controllability. |
| Outcome: | The proposed prior enjoys expressivity and controllability and can be used in language modeling and controlled text generation. |
Challenges to Open-Domain Constituency Parsing (2022.findings-acl)
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| Challenge: | Existing findings on cross-domain constituency parsing are only made on a limited number of domains. |
| Approach: | They manually annotate a high-quality constituency treebank containing five domains and analyze challenges to open-domain constituency parsing using a set of linguistic features. |
| Outcome: | The proposed model significantly improves the performance of the proposed model on the domain-variant features. |
Going “Deeper”: Structured Sememe Prediction via Transformer with Tree Attention (2022.findings-acl)
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| Challenge: | Existing studies ignore hierarchical structures of sememes in sememe-based semantic description systems. |
| Approach: | They propose a structured sememe prediction problem to predict a sememes tree with hierarchical structures rather than a set of sememas. |
| Outcome: | The proposed model outperforms baseline models and shows its effectiveness . it predicts a sememe tree with hierarchical structures rather than a set of sememes . |
Table-based Fact Verification with Self-adaptive Mixture of Experts (2022.findings-acl)
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| Challenge: | Existing research focuses on table-based fact verification, but a new trend is extending the scope to structured evidence. |
| Approach: | They propose a mixture-of-experts neural network to recognize and execute different types of reasoning . they use a management module to decide the contribution of each expert network to the verification result . |
| Outcome: | The proposed method achieves 85.1% accuracy on the TabFact dataset, comparable with the previous state-of-the-art models. |
Investigating Data Variance in Evaluations of Automatic Machine Translation Metrics (2022.findings-acl)
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| Challenge: | Current evaluation methods focus on one dataset, e.g., Newstest dataset in each year’s WMT Metrics Shared Task. |
| Approach: | They propose to use a single dataset to evaluate the performance of automatic translation metrics. |
| Outcome: | The results show that the rankings of metrics vary when the evaluation is conducted on different datasets. |
Sememe Prediction for BabelNet Synsets using Multilingual and Multimodal Information (2022.findings-acl)
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| Challenge: | Existing sememe KBs only cover a few languages, which hinders the wide utilization of sememes. |
| Approach: | They propose to build a multilingual sememe KB based on a dictionary called BabelNet . they use multilingual synonyms, multilingual glosses and images to encode sememes . |
| Outcome: | The proposed model outperforms previous methods in terms of MAP and F1 scores. |
Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding (2022.findings-acl)
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| Challenge: | Existing approaches to event extraction are limited to a set of pre-defined types. |
| Approach: | They propose a natural language query framework that uses event types and argument roles to extract candidate triggers and arguments from input text. |
| Outcome: | The proposed framework outperforms existing methods on zero-shot event extraction. |
LEVEN: A Large-Scale Chinese Legal Event Detection Dataset (2022.findings-acl)
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Feng Yao, Chaojun Xiao, Xiaozhi Wang, Zhiyuan Liu, Lei Hou, Cunchao Tu, Juanzi Li, Yun Liu, Weixing Shen, Maosong Sun
| Challenge: | Existing legal event detection datasets only cover incomprehensive event types and have limited annotated data. |
| Approach: | They present a large-scale Chinese legal event detection dataset . they use legal events as side information to promote downstream applications . |
| Outcome: | The proposed method improves 2.2 points precision in low-resource judgment prediction and 1.5 points precision for unsupervised case retrieval. |
Analyzing Dynamic Adversarial Training Data in the Limit (2022.findings-acl)
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| Challenge: | Dynamic adversarial data collection (DADC) can be used to build models that are robust across a wide range of test inputs. |
| Approach: | They propose to run Dynamic adversarial data collection over many rounds to maximize its training-time benefits. |
| Outcome: | The proposed model makes 26% fewer errors on the premise paragraphs compared to models trained on non-adversarial examples. |
AbductionRules: Training Transformers to Explain Unexpected Inputs (2022.findings-acl)
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| Challenge: | AbductionRules is a set of natural language datasets designed to train and test generalisable abduction over natural-language knowledge bases. |
| Approach: | They propose to train and test generalisable abduction over natural-language knowledge bases by using natural language datasets to fine tune pre-trained Transformers. |
| Outcome: | The proposed models learned generalisable abduction techniques but also exploited the structure of the datasets. |
On the Importance of Data Size in Probing Fine-tuned Models (2022.findings-acl)
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| Challenge: | Several studies have investigated the reasons behind the effectiveness of fine-tuning, usually through the lens of probing. |
| Approach: | They propose to investigate the reasons behind the effectiveness of fine-tuning by examining the impact of data size on the extent of encoded linguistic knowledge. |
| Outcome: | The proposed probes show that the size of the training data affects the recoverability of the changes made to the model’s linguistic knowledge. |
RuCCoN: Clinical Concept Normalization in Russian (2022.findings-acl)
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Alexandr Nesterov, Galina Zubkova, Zulfat Miftahutdinov, Vladimir Kokh, Elena Tutubalina, Artem Shelmanov, Anton Alekseev, Manvel Avetisian, Andrey Chertok, Sergey Nikolenko
| Challenge: | a new dataset for clinical concept normalization in Russian is available for download . ehrs contains over 16,028 entity mentions manually linked to over 2,409 unique concepts . |
| Approach: | They present a dataset for clinical concept normalization in Russian manually annotated by medical professionals. |
| Outcome: | The proposed dataset contains 16,028 entity mentions manually linked to over 2,409 unique concepts from the Russian language part of the UMLS ontology. |
A Sentence is Worth 128 Pseudo Tokens: A Semantic-Aware Contrastive Learning Framework for Sentence Embeddings (2022.findings-acl)
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| Challenge: | Existing approaches to contrastive learning are heavily affected by superficial features like sentence length and syntax. |
| Approach: | They propose a semantic-aware contrastive learning framework for sentence embeddings that explores the pseudo-token space representation of a sentence while eliminating the impact of superficial features such as sentence length and syntax. |
| Outcome: | The proposed framework outperforms the state-of-the-art on six standard semantic textual similarity tasks while maintaining an additional queue to store the representation of sentence embeddings. |
Eider: Empowering Document-level Relation Extraction with Efficient Evidence Extraction and Inference-stage Fusion (2022.findings-acl)
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| Challenge: | Document-level relation extraction (DocRE) aims to extract semantic relations among entity pairs in a document. |
| Approach: | They propose an evidence-enhanced framework that empowers document-level relation extraction (DocRE) Eider efficiently extracts evidence and effectively fuses extracted evidence in inference. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on three benchmark datasets. |
Meta-XNLG: A Meta-Learning Approach Based on Language Clustering for Zero-Shot Cross-Lingual Transfer and Generation (2022.findings-acl)
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| Challenge: | Existing approaches to learn shareable structures from low-resource languages are limited in the zero-shot setting. |
| Approach: | They propose a meta-learning framework to learn shareable structures from typologically diverse languages based on meta- learning and language clustering. |
| Outcome: | The proposed framework is able to learn shareable structures from typologically diverse languages with limited annotated data. |
MR-P: A Parallel Decoding Algorithm for Iterative Refinement Non-Autoregressive Translation (2022.findings-acl)
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| Challenge: | Non-autoregressive neural machine translation models remove dependency between tokens in the target sentence and generate all tokens on parallel . |
| Approach: | They propose a non-autoregressive neural machine translation model that decodes with the Mask-Predict algorithm which iteratively refines the output. |
| Outcome: | The proposed algorithm increases the performance of the WMT’14 translation task by 1.39 points. |
Open Relation Modeling: Learning to Define Relations between Entities (2022.findings-acl)
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| Challenge: | Existing systems identify related entities but do not provide features for exploring relations between entities. |
| Approach: | They propose to teach machines to generate definition-like relation descriptions by letting them learn from defining entities. |
| Outcome: | The proposed model can generate definition-like relation descriptions that capture the representative characteristics of entities. |
A Slot Is Not Built in One Utterance: Spoken Language Dialogs with Sub-Slots (2022.findings-acl)
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| Challenge: | Sub-Slot based task-oriented dialogs provide slot values segment by segment over multiple turns. |
| Approach: | They define a task called Sub-Slot based Task-Oriented Dialog (SSTOD) they build a Chinese dialog dataset SSD for boosting research on SSTOD. |
| Outcome: | The proposed task is called Sub-Slot based Task-Oriented Dialog (SSTOD) it includes 40K dialogs and 500K utterances from Chinese names, phone numbers, ID numbers and license plate numbers . the dataset is well annotated with sub-slot values, slot values, dialog states and actions . |
Towards Transparent Interactive Semantic Parsing via Step-by-Step Correction (2022.findings-acl)
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| Challenge: | Existing studies on semantic parsing focus on mapping a natural-language utterance to a logical form (LF) but natural language may contain ambiguity and variability, making this challenge difficult. |
| Approach: | They propose an interactive semantic parsing framework that explains the predicted LF step by step in natural language and enables the user to make corrections through natural-language feedback for individual steps. |
| Outcome: | The proposed framework improves parsing accuracy and transparency in a crowdsourced dialogue dataset. |
MINER: Multi-Interest Matching Network for News Recommendation (2022.findings-acl)
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| Challenge: | Existing methods learn a single user embedding from user’s historical behaviors to represent the reading interest. |
| Approach: | They propose a poly attention scheme to learn multiple interest vectors for each user, which encodes the different aspects of user interest. |
| Outcome: | The proposed approach significantly outperforms existing state-of-the-art methods on the MIND news recommendation benchmark. |
KSAM: Infusing Multi-Source Knowledge into Dialogue Generation via Knowledge Source Aware Multi-Head Decoding (2022.findings-acl)
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| Challenge: | Existing knowledge-enhanced methods only seek knowledge from a single source, resulting in insufficient coverage of a given knowledge source. |
| Approach: | They propose to use multiple independent decoder heads to infuse multi-source knowledge into dialogue generation more efficiently by incorporating external knowledge into the dialogue generation. |
| Outcome: | The proposed approach overcomes three challenges in infusing multi-source knowledge into dialogue generation more efficiently. |
Towards Responsible Natural Language Annotation for the Varieties of Arabic (2022.findings-acl)
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| Challenge: | In NLP, there is a tendency to aim for broader coverage, often overlooking cultural and (socio)linguistic nuance. |
| Approach: | They propose a playbook for responsible dataset creation for polyglossic, multidialectal languages . they focus on Arabic annotation of social media content as an example . |
| Outcome: | The proposed model is based on Arabic annotation of social media content. |
Dynamically Refined Regularization for Improving Cross-corpora Hate Speech Detection (2022.findings-acl)
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| Challenge: | Hate speech classifiers exhibit performance degradation when evaluated on datasets different from the source. |
| Approach: | They propose to automatically identify and reduce spurious correlations using attribution methods with dynamic refinement of the list of terms that need to be regularized during training. |
| Outcome: | The proposed method improves performance across corpora and on different datasets. |
Towards Large-Scale Interpretable Knowledge Graph Reasoning for Dialogue Systems (2022.findings-acl)
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| Challenge: | Existing systems that require extensive labor to process user requests are limited in their reasoning capabilities and require extensive manual effort to design. |
| Approach: | They propose a method that allows a transformer model to walk on a large-scale knowledge graph to generate responses by reasoning over differentiable knowledge graphs. |
| Outcome: | The proposed method allows a transformer model to walk on a large-scale knowledge graph to generate responses. |
MDERank: A Masked Document Embedding Rank Approach for Unsupervised Keyphrase Extraction (2022.findings-acl)
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| Challenge: | Keyphrase extraction (KPE) extracts phrases in a document that provide a concise summary of the core content. |
| Approach: | They propose an unsupervised keyphrase extraction method that ranks candidates by similarity between embeddings of source document and masked document. |
| Outcome: | The proposed method outperforms state-of-the-art methods on six benchmarks . it achieves average 3.53 improvement over the existing method . |
Visualizing the Relationship Between Encoded Linguistic Information and Task Performance (2022.findings-acl)
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| Challenge: | Recent studies show that encoding more syntactic information does not lead to better performance. |
| Approach: | They propose a method to optimize pareto-optimal models by formalizing it as a multi-objective optimization problem. |
| Outcome: | The proposed method is better than a baseline method on two NLP tasks. |
Efficient Argument Structure Extraction with Transfer Learning and Active Learning (2022.findings-acl)
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| Challenge: | Identifying and understanding the argumentative discourse structure in text has been a critical task in argument mining. |
| Approach: | They propose a context-aware Transformer-based argument structure prediction model that outperforms models that rely on features or only encode limited contexts. |
| Outcome: | The proposed model outperforms models that rely on features or encode limited contexts on five domains and on peer reviews on five different domains. |
Plug-and-Play Adaptation for Continuously-updated QA (2022.findings-acl)
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| Challenge: | Existing tasks to assess LMs’ efficacy as KBs do not adequately consider multiple large-scale updates. |
| Approach: | They propose a task where multiple large-scale updates are made to language models and plug-in modules are used to handle the updates. |
| Outcome: | The proposed method outperforms existing methods on zsRE QA and NQ datasets and is 4x more effective in terms of updates/forgets ratio compared to a fine-tuning baseline. |
Reinforced Cross-modal Alignment for Radiology Report Generation (2022.findings-acl)
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| Challenge: | Medical images are widely used in clinical decision-making, where writing radiology reports can be enhanced by automatic solutions to alleviate physicians’ workload. |
| Approach: | They propose an approach with reinforcement learning over a cross-modal memory to better align visual and textual features for radiology report generation. |
| Outcome: | The proposed approach improves cross-modal alignment on two English radiology report datasets and human evaluation confirms the results. |
What Works and Doesn’t Work, A Deep Decoder for Neural Machine Translation (2022.findings-acl)
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| Challenge: | Deep learning has demonstrated performance advantages in a wide range of natural language processing tasks. |
| Approach: | They propose to deepen the decoder layer in a Transformer model to reduce the difficulty of deep learning. |
| Outcome: | The proposed method can deepen the model on both the encoder and decoder at the same time, resulting in a deeper model and improved performance. |
SyMCoM - Syntactic Measure of Code Mixing A Study Of English-Hindi Code-Mixing (2022.findings-acl)
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| Challenge: | Recent work on code mixing in computational settings has leveraged social media code mixed texts to train NLP models. |
| Approach: | They propose to use language ID tags to measure syntactic variety in code-mixed text and their relationship with computational model performance. |
| Outcome: | The proposed measure can be applied to English(en)-hindi(hi) code-mixed datasets and compares them with other measures. |
HybriDialogue: An Information-Seeking Dialogue Dataset Grounded on Tabular and Textual Data (2022.findings-acl)
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| Challenge: | Existing datasets focused on multiturn dialogue systems focus on text or table information. |
| Approach: | They propose a dataset that consists of crowdsourced conversations grounded on Wikipedia text and tables. |
| Outcome: | The proposed dataset shows that there is still ample opportunity for improvement in the current state of dialogue systems. |
NEWTS: A Corpus for News Topic-Focused Summarization (2022.findings-acl)
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| Challenge: | Existing benchmarking corpora provide concordant pairs of full and abridged versions of Web, news or professional content. |
| Approach: | They propose a topical summarization corpus called NEWTS that is annotated via crowd-sourcing. |
| Outcome: | The proposed model can condition summaries on a desired range of themes . the proposed model outperforms Lead-3 baselines on most benchmark datasets . |
Classification without (Proper) Representation: Political Heterogeneity in Social Media and Its Implications for Classification and Behavioral Analysis (2022.findings-acl)
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| Challenge: | Prior work has shown that partisan leanings can be inferred from a diverse set of behavioral characteristics such as text, social networks, and even community participation. |
| Approach: | They test this assumption and show that commonly-used models do not generalize . they also show that political users are more toxic on the platform and inter-party interactions are even more toxic . |
| Outcome: | The proposed models do not generalize, indicating heterogeneous political users. |
Toward More Meaningful Resources for Lower-resourced Languages (2022.findings-acl)
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| Challenge: | a new position paper examines how meaningful resources for lower-resourced languages should be developed in connection with the speakers of those languages. |
| Approach: | They propose a position paper on how meaningful resources should be developed for lower-resourced languages . they examine the contents of Wikidata for a few lower-rsourced languages and examine quality issues . |
| Outcome: | The proposed approach is based on the findings of a recent study on the use of multilingual resources in language technology development. |
Better Quality Estimation for Low Resource Corpus Mining (2022.findings-acl)
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| Challenge: | State-of-the-art Quality Estimation models lack robustness to out-of domain examples. |
| Approach: | They propose a method that uses multitask training, data augmentation and contrastive learning to achieve better and more robust QE performance. |
| Outcome: | The proposed method improves QE performance significantly in the MLQE challenge and the robustness of QE models when tested in the Parallel Corpus Mining setup. |
End-to-End Segmentation-based News Summarization (2022.findings-acl)
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| Challenge: | Existing summarization systems only provide one genetic summary of the whole article, making it difficult for users to navigate the reading. |
| Approach: | They propose a task of segmenting a news article into multiple sections and generating the corresponding summary to each section. |
| Outcome: | The proposed model outperforms state-of-the-art models on a 27k news article dataset . it can jointly segment a document and produce the summary for each section . |
Fast Nearest Neighbor Machine Translation (2022.findings-acl)
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| Challenge: | Fast kNN-MT uses the entire corpus as the datastore for the nearest neighbor search . knn-MT is two-orders slower than vanilla MT models . |
| Approach: | They propose a fast kNN-MT model that uses the entire corpus as the datastore for nearest neighbor search. |
| Outcome: | The proposed model is two-orders faster than kNN-MT and is only two times slower than the standard model. |
Extracting Latent Steering Vectors from Pretrained Language Models (2022.findings-acl)
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| Challenge: | Prior work on controllable text generation has focused on learning how to control language models through trainable decoding, smart-prompt design, or fine-tuning based on a desired objective. |
| Approach: | They propose to extract latent vectors directly from pretrained language model decoders without fine-tuning. |
| Outcome: | The proposed approach generates a target sentence nearly perfectly for English sentences . it outperforms pooled hidden states of models on a textual similarity benchmark . |
Domain Generalisation of NMT: Fusing Adapters with Leave-One-Domain-Out Training (2022.findings-acl)
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| Challenge: | Recent advances in neural machine translation (NMT) research have found NMT models sensitive to distribution shift and adversarial examples. |
| Approach: | They propose a leave-one-domain-out training strategy that learns to combine domain-specific parameters to avoid information leaking. |
| Outcome: | The proposed method outperforms baselines on three language pairs on average. |
Reframing Instructional Prompts to GPTk’s Language (2022.findings-acl)
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| Challenge: | Using reframing techniques, we find that instructional prompts are easier to follow for Language Models (LMs) |
| Approach: | They propose reframing techniques for manual reformulation of prompts into more effective ones . they compare performance of LMs prompted with reframed instructions on 12 NLP tasks . |
| Outcome: | The reframing techniques used for prompt reformulation improve performance on 12 tasks . the techniques boost performance on LMs with different sizes compared with original prompts . |
Read Top News First: A Document Reordering Approach for Multi-Document News Summarization (2022.findings-acl)
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Chao Zhao, Tenghao Huang, Somnath Basu Roy Chowdhury, Muthu Kumar Chandrasekaran, Kathleen McKeown, Snigdha Chaturvedi
| Challenge: | Existing methods for extracting multi-document news summarization neglect relative importance of documents. |
| Approach: | They propose to concatenate all documents into a single meta-document and then summarize it using an SDS model. |
| Outcome: | The proposed approach outperforms state-of-the-art methods with more complex architectures. |
Human Language Modeling (2022.findings-acl)
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| Challenge: | Existing language modeling models treat text sequences as if they were created independently. |
| Approach: | They propose a hierarchical extension to the language modeling problem whereby a human-level exists to connect sequences of documents and capture the notion that human language is moderated by changing human states. |
| Outcome: | The proposed model outperforms the current state-of-the-art in terms of language modeling and fine-tuning for 4 downstream tasks spanning document- and user-levels. |
Inverse is Better! Fast and Accurate Prompt for Few-shot Slot Tagging (2022.findings-acl)
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| Challenge: | Recent results show that prompting methods are inefficient for slot tagging tasks . inverse prompting only requires a one-turn prediction for each slot type . |
| Approach: | They propose an inverse prompting paradigm that reversely predicts slot values given slot types . the method is faster and significantly improves the effect on 10-shot setting . |
| Outcome: | The proposed method improves over 6.1 F1-scores on 10-shot setting and achieves new state-of-the-art performance. |
Cross-Modal Cloze Task: A New Task to Brain-to-Word Decoding (2022.findings-acl)
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| Challenge: | Existing work on decoding language from non-invasive brain activity is limited due to noisy nature of brain recordings. |
| Approach: | They propose a cross-modal cloze task to predict a word from a neural image . they use a pre-trained language model to leverage the pre-training language model . |
| Outcome: | The proposed method outperforms baselines on 20 participants from two brain imaging datasets. |
Mitigating Gender Bias in Distilled Language Models via Counterfactual Role Reversal (2022.findings-acl)
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Umang Gupta, Jwala Dhamala, Varun Kumar, Apurv Verma, Yada Pruksachatkun, Satyapriya Krishna, Rahul Gupta, Kai-Wei Chang, Greg Ver Steeg, Aram Galstyan
| Challenge: | Language models excel at generating coherent text, but can be biased in multiple ways, including the unfounded association of male and female genders with gender-neutral professions. |
| Approach: | They propose to modify teacher probabilities and augment the training set to learn a fair model during knowledge distillation by modifying teacher probability and augmenting the training sets. |
| Outcome: | The proposed approach reduces gender disparity in open-ended text generated from the distilled and finetuned models with only a minor compromise in utility. |
Domain Representative Keywords Selection: A Probabilistic Approach (2022.findings-acl)
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| Challenge: | a probabilistic approach to select a subset of a target domain representative keywords is crucial for many downstream tasks in natural language processing. |
| Approach: | They propose a probabilistic approach to select a subset of a target domain representative keywords from a candidate set, contrasting with a context domain. |
| Outcome: | The proposed approach provides more importance to distinctive keywords than common keywords contrasting with the context domain. |
Hierarchical Inductive Transfer for Continual Dialogue Learning (2022.findings-acl)
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| Challenge: | Existing frameworks for learning and deployment of neural dialogue models have been used for online chit-chat scenarios. |
| Approach: | They propose a hierarchical inductive transfer framework to learn and deploy dialogue skills continually and efficiently. |
| Outcome: | The proposed framework achieves comparable performance under deployment-friendly model capacity. |
Why Exposure Bias Matters: An Imitation Learning Perspective of Error Accumulation in Language Generation (2022.findings-acl)
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| Challenge: | Current language generation models suffer from issues such as repetition, incoherence, and hallucinations . |
| Approach: | They propose to analyze exposure bias from an imitation learning perspective and prove it is a problem . they show that exposure bias leads to an accumulation of errors during generation . |
| Outcome: | The proposed model fails to capture errors during generation and poor generation quality. |
Question Answering Infused Pre-training of General-Purpose Contextualized Representations (2022.findings-acl)
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| Challenge: | Existing pretraining objectives for question answering (QA) are not optimized for being immediately useful without fine-tuning. |
| Approach: | They propose a pre-training objective based on question answering (QA) that is based more directly on context. |
| Outcome: | The proposed model matches predictions of a more accurate cross-encoder model on 80 million synthesized QA pairs and achieves large improvements over previous state-of-the-art models on paraphrase detection and fewshot named entity recognition. |
Automatic Song Translation for Tonal Languages (2022.findings-acl)
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| Challenge: | Existing automatic song translation systems for tonal languages do not match the number of notes and beat the original rhythm of the song. |
| Approach: | They propose three criteria for effective AST: preserving meaning, singability and intelligibility. |
| Outcome: | The proposed system balances semantics and singability with human evaluations. |
Read before Generate! Faithful Long Form Question Answering with Machine Reading (2022.findings-acl)
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| Challenge: | Long-form question answering (LFQA) generates a paragraph-length answer for a given question. |
| Approach: | They propose a framework that jointly models answer generation and machine reading. |
| Outcome: | The proposed model generates a more factually accurate answer from millions of documents retrieved from a large dataset. |
A Simple yet Effective Relation Information Guided Approach for Few-Shot Relation Extraction (2022.findings-acl)
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| Challenge: | Existing approaches to introduce relation information into the model are limited by labeling and data scarcity. |
| Approach: | They propose a direct addition approach to introduce relation information into a model by concatenating two views of relations and adding them to the original prototype. |
| Outcome: | The proposed approach improves on the benchmark dataset FewRel 1.0 and shows comparable results to the state-of-the-art. |
MIMICause: Representation and automatic extraction of causal relation types from clinical notes (2022.findings-acl)
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| Challenge: | Extracted causal information from clinical notes can be combined with structured EHR data such as demographics, diagnoses, and medications. |
| Approach: | They propose to annotate clinical notes and develop an annotated corpus and provide baseline scores to identify types and direction of causal relations between a pair of biomedical concepts. |
| Outcome: | The proposed annotation guidelines achieved a high inter-annotator agreement and a macro F1 score on the clinical text. |
Compressing Sentence Representation for Semantic Retrieval via Homomorphic Projective Distillation (2022.findings-acl)
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| Challenge: | Existing pre-trained language models produce large sentence embeddings, resulting in performance gap between large and small models. |
| Approach: | They propose a method that augments a small Transformer encoder model with learnable projection layers to produce compact sentences while mimicking a large pre-trained language model to retain the sentence representation quality. |
| Outcome: | The proposed method achieves 2.7-4.5 points performance gain on STS and SR tasks while maintaining the quality of the pre-trained language models. |
Debiasing Event Understanding for Visual Commonsense Tasks (2022.findings-acl)
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| Challenge: | a recent study shows that object-based event understanding is purely likelihood-based, leading to incorrect event prediction. |
| Approach: | They propose to mitigate object-based event understanding by optimizing aggregation with association-based prediction. |
| Outcome: | The proposed approach improves visual commonsense reasoning tasks by combining do-calculus with association-based prediction. |
Fact-Tree Reasoning for N-ary Question Answering over Knowledge Graphs (2022.findings-acl)
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| Challenge: | Current Question Answering over Knowledge Graphs (KGQA) tasks focus on binary facts, but neglect n-ary facts. |
| Approach: | They propose a new fact-tree reasoning framework that transforms the question into a fact tree and performs iterative fact reasoning on the fact tree to infer the correct answer. |
| Outcome: | The proposed framework performs iterative fact reasoning on the fact tree to infer the correct answer. |
DeepStruct: Pretraining of Language Models for Structure Prediction (2022.findings-acl)
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| Challenge: | Pretrained language models perform structural understanding tasks that focus on understanding one aspect of the text. |
| Approach: | They propose a method for improving the structural understanding abilities of language models by pretraining them to generate structures from the text on task-agnostic corpora. |
| Outcome: | The proposed model performs state-of-the-art on 21 of 28 datasets. |
The Change that Matters in Discourse Parsing: Estimating the Impact of Domain Shift on Parser Error (2022.findings-acl)
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| Challenge: | Discourse analysis is very low on texts outside of the training distribution’s coverage, diminishing the practical utility of existing models. |
| Approach: | They propose to use a distribution shift statistic to estimate the error-gap of a discourse model and to use it to estimate it. |
| Outcome: | The proposed model can be estimated via distribution shift but does not correlate with change in the observed error of a classifier (i.e. error-gap). |
Mukayese: Turkish NLP Strikes Back (2022.findings-acl)
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| Challenge: | Having sufficient resources for language X lifts it from the under-resourced languages class, but not necessarily from the researched class. |
| Approach: | They propose a set of NLP benchmarks for the Turkish language that contains several NLP tasks. |
| Outcome: | The proposed benchmarks outperform previous work significantly in the Turkish language. |
Virtual Augmentation Supported Contrastive Learning of Sentence Representations (2022.findings-acl)
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| Challenge: | Despite profound successes, contrastive representation learning relies on carefully designed data augmentations using domain-specific knowledge. |
| Approach: | They propose a virtual augmentation supported Contrastive Learning of sentence representations . they approximate the neighborhood of an instance via its K-nearest in-batch neighbors . |
| Outcome: | The proposed model outperforms existing methods on a wide range of downstream tasks. |
MoEfication: Transformer Feed-forward Layers are Mixtures of Experts (2022.findings-acl)
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| Challenge: | Recent work has shown that feed-forward networks (FFNs) in pre-trained Transformers are a key component, storing various linguistic and factual knowledge. |
| Approach: | They propose to convert a model into its MoE version with the same parameters and build expert routers to decide which experts will be used for each input. |
| Outcome: | The proposed model can use 10% to 30% of FFN parameters while maintaining over 95% original performance. |
DS-TOD: Efficient Domain Specialization for Task-Oriented Dialog (2022.findings-acl)
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| Challenge: | Recent work shows that self-supervised dialog-specific pretraining on large conversational datasets yields substantial gains over traditional language modeling (LM) pretraining. |
| Approach: | They propose a resource-efficient and modular domain specialization by means of domain adapters in which domain knowledge is encoded. |
| Outcome: | The proposed framework extracts domain-specific terms and then uses them to build DomainCC and DomainReddit resources based on masked language modeling and response selection objectives. |
Distinguishing Non-natural from Natural Adversarial Samples for More Robust Pre-trained Language Model (2022.findings-acl)
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| Challenge: | Recent studies on adversarial attacks achieve high success rates against PrLMs, claiming that they are not robust. |
| Approach: | They propose to use anomaly detector to evaluate PrLMs with more natural adversarial samples to evaluate their robustness. |
| Outcome: | The proposed method can be used to defend all types of attacks and achieve higher accuracy on adversarial samples and compliant samples than other defense frameworks. |
Learning Adaptive Axis Attentions in Fine-tuning: Beyond Fixed Sparse Attention Patterns (2022.findings-acl)
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Zihan Wang, Jiuxiang Gu, Jason Kuen, Handong Zhao, Vlad Morariu, Ruiyi Zhang, Ani Nenkova, Tong Sun, Jingbo Shang
| Challenge: | Adaptive Axis Attention learns different attention patterns for each task and model layer . sparse attention patterns do not improve the run time of the models but they reduce model memory requirements . |
| Approach: | They propose a method that learns different attention patterns for each Transformer layer . they propose 'adaptive axis attention' method that identifies important tokens . |
| Outcome: | The proposed method does not require pre-training to accommodate sparse attention patterns. |
Using Interactive Feedback to Improve the Accuracy and Explainability of Question Answering Systems Post-Deployment (2022.findings-acl)
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| Challenge: | Existing work on question answering focuses on the pre-deployment stage; building an accurate model for deployment. |
| Approach: | They collect feedback from users and train a neural model with the feedback data. |
| Outcome: | The proposed model can explain the correctness or incorrectness of an answer. |
To be or not to be an Integer? Encoding Variables for Mathematical Text (2022.findings-acl)
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| Challenge: | a number of natural language inference models are limited in interpreting mathematical knowledge written in Natural Language . a variable's meaning is determined exclusively by its defining type, i.e., its context . |
| Approach: | They propose a method that can create context-based representations for variables . they propose 'variable slot' approach which can be used to model variables based on their meaning . |
| Outcome: | The proposed model can be used to represent variables in natural language . it can be applied to a task of variable typing and create context-based representations for variables . |
GRS: Combining Generation and Revision in Unsupervised Sentence Simplification (2022.findings-acl)
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| Challenge: | Existing methods for sentence simplification are supervised or unsupervised . paraphrasing captures complex edit operations, while revision-based methods provide more control and interpretability. |
| Approach: | They propose an unsupervised approach to sentence simplification that combines text generation and text revision. |
| Outcome: | The proposed method improves on the Newsela and ASSET datasets. |
BPE vs. Morphological Segmentation: A Case Study on Machine Translation of Four Polysynthetic Languages (2022.findings-acl)
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| Challenge: | Morphologically rich polysynthetic languages present a challenge for NLP systems due to data sparsity. |
| Approach: | They propose to use subword segmentation to reduce data sparsity in polysynthetic languages . they compare supervised and unsupervised morphological segmentation methods to Byte-Pair Encodings . |
| Outcome: | The proposed methods outperform BPEs in MT tasks for all language pairs except for Nahuatl . the proposed methods are more efficient than supervised methods, but less sparse in fusional languages. |
Distributed NLI: Learning to Predict Human Opinion Distributions for Language Reasoning (2022.findings-acl)
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| Challenge: | Using distributed NLI, we show that models can capture human judgement distribution more effectively than the softmax baseline. |
| Approach: | They propose a new NLU task to predict the distribution of human judgements . they propose Monte Carlo, Deep Ensemble, Re-Calibration and Distribution Distillation methods to capture human judgement distributions. |
| Outcome: | The proposed methods perform better than the softmax baseline, but the results are still far below the estimated human upper-bound. |
Morphological Processing of Low-Resource Languages: Where We Are and What’s Next (2022.findings-acl)
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Adam Wiemerslage, Miikka Silfverberg, Changbing Yang, Arya McCarthy, Garrett Nicolai, Eliana Colunga, Katharina Kann
| Challenge: | Existing models for morphological processing are not suitable for low-resource languages, but they are still lacking in the field of computational morphology. |
| Approach: | They propose to bridge two unsupervised models to understand a language’s morphology from raw text alone and propose to use them to improve their models. |
| Outcome: | The proposed models perform reasonably, but there is room for improvement. |
Learning and Evaluating Character Representations in Novels (2022.findings-acl)
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| Challenge: | Recent advances in word embeddings have proven successful in learning entity representations from short texts but do not capture full book-level information. |
| Approach: | They propose two novel ways to learn fixed-length vector representations of characters from novels . they use graph neural network-based embeddings from a full corpus-based character network . |
| Outcome: | The proposed methods outperform text-based embeddings in four tasks. |
Answer Uncertainty and Unanswerability in Multiple-Choice Machine Reading Comprehension (2022.findings-acl)
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| Challenge: | Machine reading comprehension (MRC) systems focus on selecting the correct answer to a question given a context paragraph. |
| Approach: | They propose to use machine reading comprehension (MRC) to assess the ability of systems to understand natural language. |
| Outcome: | The proposed system outperforms a system built with an NOA option . the results show that the system is not confident about the NOA choice . |
Measuring the Language of Self-Disclosure across Corpora (2022.findings-acl)
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| Challenge: | Existing models that estimate self-disclosure from language are poorly generalized due to variations in corpora and labeling instructions. |
| Approach: | They build single-task models on five self-disclosure corpora and use them to predict self-declaration across corpors. |
| Outcome: | The proposed model predicts self-disclosure across corpora, but the results are poor for out-of-corpora models. |
When Chosen Wisely, More Data Is What You Need: A Universal Sample-Efficient Strategy For Data Augmentation (2022.findings-acl)
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| Challenge: | Existing DA methods naively add a certain number of augmented samples without considering the quality and the added computational cost of these samples. |
| Approach: | They propose a data-augmented DA technique that generates or reweights augmented samples . they say it is faster to train and can be plugged into any DA method . |
| Outcome: | The proposed technique is faster to train and more efficient than existing methods. |
Explaining Classes through Stable Word Attributions (2022.findings-acl)
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| Challenge: | Input saliency methods have become popular for explaining predictions of deep learning models, but there has been little work investigating methods for aggregating prediction-level explanations to the class level. |
| Approach: | They propose a method to aggregate prediction-level explanations to the class level using XLM-R and Integrated Gradients input attribution methods. |
| Outcome: | The proposed method extracts keyword lists of classes from text classification tasks and evaluates them on web register data. |
What to Learn, and How: Toward Effective Learning from Rationales (2022.findings-acl)
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| Challenge: | Increasing interest in learning from rationales has led to the use of human-annotated explanations to inject useful inductive biases into models. |
| Approach: | They propose several novel loss functions and learning strategies to exploit human rationales to augment model prediction accuracy. |
| Outcome: | The proposed learning strategies improve on three datasets with human rationales and show that they are more efficient than baselines. |
Listening to Affected Communities to Define Extreme Speech: Dataset and Experiments (2022.findings-acl)
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| Challenge: | XTREMESPEECH dataset contains 20,297 social media passages from Brazil, Germany, India and Kenya . |
| Approach: | They propose a hate speech dataset containing 20,297 social media passages from Brazil, Germany, India and Kenya. |
| Outcome: | The proposed dataset contains 20,297 social media passages from Brazil, Germany, India and Kenya. |
Entropy-based Attention Regularization Frees Unintended Bias Mitigation from Lists (2022.findings-acl)
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| Challenge: | E.g., neural hate speech detection models are strongly influenced by identity terms like gay, or women, resulting in false positives, severe unintended bias, and lower performance. |
| Approach: | They propose a knowledge-free Entropy-based Attention Regularization (EAR) approach to discourage overfitting to training-specific terms. |
| Outcome: | The proposed model matches or exceeds state-of-the-art performance for hate speech classification and bias metrics on three benchmark corpora in English and Italian. |
From BERT‘s Point of View: Revealing the Prevailing Contextual Differences (2022.findings-acl)
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| Challenge: | BERTology is a new approach to understanding the inner workings of large pretraining language models. |
| Approach: | They propose to invert the probing design to analyze the prevailing differences and clusters in BERT’s high dimensional space by extracting coarse features from masked token representations and predicting them by probing models with access to only partial information. |
| Outcome: | The proposed method extracts coarse features from masked token representations and predicts them by probing models with access to only partial information. |
Learning Bias-reduced Word Embeddings Using Dictionary Definitions (2022.findings-acl)
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| Challenge: | Existing word embeddings have undesirable gender, racial, and religious biases . DD-GloVe is a train-time debiasing algorithm that uses dictionary definitions based on word definitions. |
| Approach: | They propose a dictionary-guided loss function that encourages word embeddings to be similar to their relatively neutral dictionary definition representations. |
| Outcome: | The proposed algorithm can learn word embeddings by leveraging dictionary definitions. |
Knowledge Graph Embedding by Adaptive Limit Scoring Loss Using Dynamic Weighting Strategy (2022.findings-acl)
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| Challenge: | Existing knowledge graph embedding models use a loss framework to distinguish between correct and incorrect triplets. |
| Approach: | They propose a loss framework that reweights each triplet to highlight the less-optimized triplets. |
| Outcome: | The proposed method performs on several knowledge graph embedding models, including TransE, TransH and ComplEx. |
OCR Improves Machine Translation for Low-Resource Languages (2022.findings-acl)
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| Challenge: | Despite many recent successes, Machine Translation still lacks support or fails to achieve good performance for most low-resource languages. |
| Approach: | They propose a benchmark to evaluate OCR systems on low-resource languages and low- resource scripts. |
| Outcome: | The proposed benchmark evaluates state-of-the-art OCR systems on low-resource languages and low-rural scripts. |
CoCoLM: Complex Commonsense Enhanced Language Model with Discourse Relations (2022.findings-acl)
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| Challenge: | Large-scale pre-trained language models have demonstrated strong knowledge representation ability, but struggle with complex commonsense knowledge that involves multiple eventualities. |
| Approach: | They propose to help pre-trained language models better incorporate complex commonsense knowledge that involves multiple eventualities. |
| Outcome: | The proposed model can learn to use the memorized knowledge for different tasks and achieve outstanding performance on many downstream natural language processing (NLP) tasks. |
Learning to Robustly Aggregate Labeling Functions for Semi-supervised Data Programming (2022.findings-acl)
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Ayush Maheshwari, Krishnateja Killamsetty, Ganesh Ramakrishnan, Rishabh Iyer, Marina Danilevsky, Lucian Popa
| Challenge: | supervised machine learning requires large amounts of labeled data to train models. |
| Approach: | They propose a framework to generate human-interpretable labeling functions . they propose to learn a model on the same labeled dataset and unlabeled data . |
| Outcome: | The proposed framework outperforms prior approaches on several text classification datasets. |
Multi-Granularity Semantic Aware Graph Model for Reducing Position Bias in Emotion Cause Pair Extraction (2022.findings-acl)
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| Challenge: | Existing methods to extract emotions and causes as pairs neglect effective semantic connections between distant clauses, leading to poor generalization ability towards position-insensitive data. |
| Approach: | They propose a novel multi-granularity semantic-aware Graph model to integrate fine-grained and coarse-grain semantic features together without regard to distance limitation. |
| Outcome: | The proposed model outperforms existing models significantly in position-insensitive data. |
Cross-lingual Inference with A Chinese Entailment Graph (2022.findings-acl)
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| Challenge: | Existing work on predicate entailment detection from typed open relation triples has not been able to detect predicates. |
| Approach: | They propose a pipeline for building Chinese entailment graphs using an open relation extraction method. |
| Outcome: | The proposed pipeline outperforms monolingual and Chinese entailment graphs on a parallel dataset. |
Multi-task Learning for Paraphrase Generation With Keyword and Part-of-Speech Reconstruction (2022.findings-acl)
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| Challenge: | PGKPR is a deep learning approach to generate paraphrases using key semantics of the source sentence. |
| Approach: | They propose a model with keyword and part-of-speech reconstruction for paraphrase generation using deep learning. |
| Outcome: | The proposed model outperforms comparative models on two commonly-used datasets. |
MDCSpell: A Multi-task Detector-Corrector Framework for Chinese Spelling Correction (2022.findings-acl)
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| Challenge: | Chinese Spelling Correction (CSC) is a task to detect and correct misspelled characters in Chinese texts. |
| Approach: | They propose a general detector-corrector multi-task framework which exploits the visual and phonological features of the misspelled characters and minimizes their misleading impact on the context. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on Chinese Spelling Correction tasks. |
S2SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers (2022.findings-acl)
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| Challenge: | Existing graph-based encoders for text-to-SQL do not model the syntax of natural language questions. |
| Approach: | They propose to inject Syntax to question-Schema graph encoder for text-to-SQL parsers and employ the decoupling constraint to induce diverse relational edge embedding. |
| Outcome: | The proposed approach outperforms all existing methods when pre-training models are used, resulting in a performance ranks first on the Spider leaderboard. |
Constructing Open Cloze Tests Using Generation and Discrimination Capabilities of Transformers (2022.findings-acl)
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| Challenge: | Existing open cloze tests are laborious to design because they require a large number of variables to predict the distribution of words in a text passage. |
| Approach: | They propose a transformer-based model that exploits generation and discrimination capabilities to improve performance. |
| Outcome: | The proposed model outperforms previous work and baselines in 82% accuracy and can be used as a future benchmark. |
Co-training an Unsupervised Constituency Parser with Weak Supervision (2022.findings-acl)
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| Challenge: | Existing methods for unsupervised parsing that use bootstrapping classifiers to identify if a node dominates a span are lacking. |
| Approach: | They propose a method for unsupervised parsing that relies on bootstrapping classifiers to identify if a node dominates a specific span. |
| Outcome: | The proposed method achieves 63.1 F1 on the English test set and new state-of-the-art on treebanks for Chinese and Japanese. |
HiStruct+: Improving Extractive Text Summarization with Hierarchical Structure Information (2022.findings-acl)
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| Challenge: | Existing models that treat texts as linear sequences do not include hierarchical structure information. |
| Approach: | They propose to inject hierarchical structure information into an extractive summarization model by combining hierarchically structured text with a pre-trained Transformer language model. |
| Outcome: | The proposed model outperforms a baseline model on PubMed and arXiv datasets and the hierarchical structure information is not injected. |
An Isotropy Analysis in the Multilingual BERT Embedding Space (2022.findings-acl)
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| Challenge: | Existing studies have explored the advantages of multilingual pre-trained models in capturing shared linguistic knowledge. |
| Approach: | They investigate the anisotropic embedding space and outlier dimensions of the multilingual BERT model for two known issues of the monolingual models. |
| Outcome: | The proposed model has no outlier dimension and has highly anisotropic space . the results show that increasing the isotropy of multilingual space can improve its representation power and performance, similar to what had been observed for monolingual CWRs on semantic similarity tasks. |
Multi-Stage Prompting for Knowledgeable Dialogue Generation (2022.findings-acl)
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Zihan Liu, Mostofa Patwary, Ryan Prenger, Shrimai Prabhumoye, Wei Ping, Mohammad Shoeybi, Bryan Catanzaro
| Challenge: | Existing knowledge-grounded dialogue systems typically use finetuned versions of a pretrained language model and large-scale knowledge bases. |
| Approach: | They propose a multi-stage prompting approach to generate knowledgeable responses from a single pretrained LM. |
| Outcome: | The proposed model outperforms the state-of-the-art retrieval-based model in terms of knowledge relevance and correctness by 5.8% and 5%, respectively. |
DuReadervis: A Chinese Dataset for Open-domain Document Visual Question Answering (2022.findings-acl)
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| Challenge: | Open-domain question answering is a task that requires answering questions based on a collection of document images. |
| Approach: | They propose to use document images to answer questions using layouts and visual features instead of text. |
| Outcome: | The proposed approach reduces human cost and improves scalability of QA systems by incorporating layouts and visual features. |
Coloring the Blank Slate: Pre-training Imparts a Hierarchical Inductive Bias to Sequence-to-sequence Models (2022.findings-acl)
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| Challenge: | Sequence-to-sequence models fail to generalize in hierarchy-sensitive manner when performing syntactic transformations. |
| Approach: | They evaluate whether seq2seq models generalize hierarchically on two transformations . they use pre-trained models and their multilingual variants to test their generalization . |
| Outcome: | The proposed models generalize hierarchically on two transformations in English and German. |
C3KG: A Chinese Commonsense Conversation Knowledge Graph (2022.findings-acl)
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| Challenge: | Existing commonsense knowledge bases organize tuples in an isolated manner, causing problems for chatbots . |
| Approach: | They create a Chinese commonsense conversation knowledge graph which integrates social commonsensm and dialog flow information. |
| Outcome: | The proposed graph incorporates social commonsense knowledge and dialog flow information. |
Graph Neural Networks for Multiparallel Word Alignment (2022.findings-acl)
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| Challenge: | Generally, word alignment algorithms only use bitext and do not make use of the fact that many parallel corpora are multiparallel. |
| Approach: | They propose a multiparallel word alignment graph and graph neural networks to exploit it . they add and remove edges from the initial alignments and generalize the model . |
| Outcome: | The proposed method outperforms previous work on three word alignment datasets and on a downstream task. |
Sentiment Word Aware Multimodal Refinement for Multimodal Sentiment Analysis with ASR Errors (2022.findings-acl)
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| Challenge: | Existing models for multimodal sentiment analysis are limited in their capacity to be deployed in the real world. |
| Approach: | They propose a model that can dynamically refine erroneous sentiment words by leveraging multimodal sentiment clues. |
| Outcome: | The proposed model surpasses the state-of-the-art models on three datasets. |
A Novel Framework Based on Medical Concept Driven Attention for Explainable Medical Code Prediction via External Knowledge (2022.findings-acl)
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| Challenge: | Existing methods to predict medical codes from clinical notes lack interpretability due to lengthy and noisy clinical notes. |
| Approach: | They propose a framework based on medical concept driven attention to integrate external knowledge for explainable medical code prediction from clinical notes. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on a benchmark dataset showing that it is more accurate than existing methods. |
Effective Unsupervised Constrained Text Generation based on Perturbed Masking (2022.findings-acl)
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| Challenge: | Existing methods for constrained text generation stochastically sample edit positions and actions, which cause unnecessary search steps. |
| Approach: | They propose to extend perturbed masking technique to search for most incongruent token to edit and introduce four multi-aspect scoring functions to select edit action to further reduce search difficulty. |
| Outcome: | The proposed method achieves state-of-the-art in two representative tasks . it does not require supervised data, so it could be applied to different generation tasks. |
Combining (Second-Order) Graph-Based and Headed-Span-Based Projective Dependency Parsing (2022.findings-acl)
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| Challenge: | Existing graph-based methods that score dependency trees do not score dependency arcs at all. |
| Approach: | They propose a headed-span-based method that decomposes the score of a dependency tree into scores of headed spans. |
| Outcome: | The proposed method improves over first-order graph-based methods, but does not score dependency arcs at all. |
End-to-End Speech Translation for Code Switched Speech (2022.findings-acl)
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Orion Weller, Matthias Sperber, Telmo Pires, Hendra Setiawan, Christian Gollan, Dominic Telaar, Matthias Paulik
| Challenge: | Code switching (CS) is the phenomenon of interchangeably using words and phrases from different languages. |
| Approach: | They propose a new ST corpus that extends the joint transcription and translation setup. |
| Outcome: | The proposed model performs well even when no training data is used. |
A Transformational Biencoder with In-Domain Negative Sampling for Zero-Shot Entity Linking (2022.findings-acl)
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| Challenge: | Recent work on entity linking has focused on the zero-shot scenario where at test time the entity mention to be labelled is never seen during training. |
| Approach: | They propose a transformational biencoder that integrates a transform into BERT to perform a zero-shot transfer from the source domain to the target domain. |
| Outcome: | The proposed model performs a zero-shot transfer from the source domain to the target domain on a benchmark dataset and achieves new state-of-the-art. |
Finding the Dominant Winning Ticket in Pre-Trained Language Models (2022.findings-acl)
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| Challenge: | Existing studies on pre-trained language models show that they can fine-tune parameters but achieve good downstream performance. |
| Approach: | They find that a dominant winning ticket takes up 0.05% of the parameters and is transferable across different tasks. |
| Outcome: | The proposed model can achieve comparable performance with the full-parameter model, the authors show . the dominant winning ticket takes up 0.05% of the parameters, and the model is transferable across tasks, they show - the authors conclude . |
Thai Nested Named Entity Recognition Corpus (2022.findings-acl)
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Weerayut Buaphet, Can Udomcharoenchaikit, Peerat Limkonchotiwat, Attapol Rutherford, Sarana Nutanong
| Challenge: | a new dataset for Named Entity Recognition (NER) is proposed for Thailand. |
| Approach: | They propose to use Thai N-NER to extract named entities from text . they propose to include a nested structure that can be used to improve NER . |
| Outcome: | The proposed dataset is the largest non-English N-NER dataset and the first non- English one with fine-grained classes. |
Two-Step Question Retrieval for Open-Domain QA (2022.findings-acl)
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| Challenge: | Existing question retrieval models have shown a significant increase in inference speed but at the cost of lower QA performance compared to the retriever-reader pipeline. |
| Approach: | They propose a two-step question retrieval model with distant supervision to improve inference speed. |
| Outcome: | The proposed model significantly increases the performance of existing question retrieval models with a negligible loss on inference speed. |
Semantically Distributed Robust Optimization for Vision-and-Language Inference (2022.findings-acl)
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| Challenge: | Existing methods to integrate linguistic knowledge into training pipelines are under-explored. |
| Approach: | They propose a model-agnostic method that leverages linguistic transformations to infer a set of linguistic phenomena. |
| Outcome: | The proposed method improves on benchmark datasets with images and video and is generalizable to other V&L tasks. |
Learning from Missing Relations: Contrastive Learning with Commonsense Knowledge Graphs for Commonsense Inference (2022.findings-acl)
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| Challenge: | Existing approaches to commonsense inference lack coverage and expressive diversity of commonsensense knowledge graphs. |
| Approach: | They propose a framework that contrasts sets of semantically similar and dissimilar events . they propose 'solar' framework that can be used to learn commonsense inference . |
| Outcome: | The proposed framework outperforms the state-of-the-art commonsense transformer on commonsensense inference by 1.84% on average among 8 metrics. |
Capture Human Disagreement Distributions by Calibrated Networks for Natural Language Inference (2022.findings-acl)
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| Challenge: | Previously, it's common to disregard it as noise or as a sign of poor-quality data, as their annotations are heavily based on personal experience and opinions. |
| Approach: | They propose to capture the human disagreement distribution from the perspective of model calibration. |
| Outcome: | The proposed model can achieve competitive performance when well-calibrated, on divergence scores between predictive probability and the true human opinion distribution, and the accuracy. |
Efficient, Uncertainty-based Moderation of Neural Networks Text Classifiers (2022.findings-acl)
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| Challenge: | A series of benchmarking experiments based on three different datasets and three state-of-the-art classifiers show that our framework can improve the classification F1-scores by 5.1 to 11.2% (up to approx. 98 to 99%) |
| Approach: | They propose a semi-automated approach that passes unconfident, probably incorrect classifications to human moderators to minimize the workload. |
| Outcome: | The proposed approach can improve the classification F1-scores by 5.1 to 11.2% (up to approx. 98 to 99%) while reducing the moderation load up to 73.3% compared to a random moderation. |
Revisiting Automatic Evaluation of Extractive Summarization Task: Can We Do Better than ROUGE? (2022.findings-acl)
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| Challenge: | Existing methods to evaluate text summarization tasks using ROUGE have been criticized for lack of semantic understanding. |
| Approach: | They propose a semantic-aware metric for extractive summarization task that is semantic-based . they use CNN/DailyMail dataset to study the new metric . |
| Outcome: | The proposed metric is semantic-aware and shows higher correlation with human judgement and yields a large number of disagreements with the original ROUGE metric. |
Open Vocabulary Extreme Classification Using Generative Models (2022.findings-acl)
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Daniel Simig, Fabio Petroni, Pouya Yanki, Kashyap Popat, Christina Du, Sebastian Riedel, Majid Yazdani
| Challenge: | Extreme multi-label classification (XMC) aims at tagging content with subset of labels from an extremely large label set. |
| Approach: | They propose a model that predicts a set of labels outside of the known vocabulary by using a loss-dependent loss-based loss-free model. |
| Outcome: | The proposed model can predict labels outside the known vocabulary while performing on par with state-of-the-art solutions for known labels. |
Decomposed Meta-Learning for Few-Shot Named Entity Recognition (2022.findings-acl)
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| Challenge: | Named entity recognition systems aim at recognizing unseen entity types based on a few labeled examples. |
| Approach: | They propose a decomposed meta-learning approach to solve few-shot span detection and few- shot entity typing problems by introducing a model-agnostic meta-loop algorithm. |
| Outcome: | The proposed approach achieves superior performance over prior methods on benchmarks. |
TegTok: Augmenting Text Generation via Task-specific and Open-world Knowledge (2022.findings-acl)
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| Challenge: | Generating natural and informative texts has been a long-standing problem in NLP. |
| Approach: | They propose to augment TExt Generation via Task-specific and Open-world Knowledge in a unified framework. |
| Outcome: | The proposed model can learn what and how to generate on two text generation tasks. |
EmoCaps: Emotion Capsule based Model for Conversational Emotion Recognition (2022.findings-acl)
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| Challenge: | Existing studies on ERC focus on context modeling but ignore representation of contextual emotional tendency. |
| Approach: | They propose to use Emoformer to extract multi-modal emotion vectors from different modalities and fuse them with sentence vector to be an emotion capsule. |
| Outcome: | The proposed model outperforms the state-of-the-art models on two benchmark datasets. |
Logic-Driven Context Extension and Data Augmentation for Logical Reasoning of Text (2022.findings-acl)
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| Challenge: | Existing methods for logical reasoning of text focus on contextual semantics while struggling to explicitly model the logical inference process. |
| Approach: | They propose a logic-driven context extension framework and a data-driven augmentation algorithm that uses contrastive learning to better capture logical information. |
| Outcome: | The proposed framework outperforms existing methods on two benchmark datasets, ReClor and LogiQA. |
Transfer Learning and Prediction Consistency for Detecting Offensive Spans of Text (2022.findings-acl)
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| Challenge: | Existing models for toxic span detection only classify text snippets as offensive or not . a novel model seeks to simultaneously predict offensive words and opinion phrases . |
| Approach: | They propose a novel model that seeks to predict offensive words and opinion phrases simultaneously . they also introduce a regularization mechanism to encourage consistency of the model predictions . |
| Outcome: | The proposed model performs well compared to baselines on toxic span detection tasks . it predicts offensive words and opinion phrases to leverage inter-dependencies . |
Learning Reasoning Patterns for Relational Triple Extraction with Mutual Generation of Text and Graph (2022.findings-acl)
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| Challenge: | Existing methods focused on learning text patterns from explicit mentions but failed to extract the implicitly implied triples. |
| Approach: | They propose to construct a relational graph from a sentence and apply multi-layer graph convolutions to capture the type inference logic of the paths. |
| Outcome: | The proposed framework can find multi-hop reasoning paths and capture type inference logic with the sentence's supplementary relational expressions. |
Document-Level Event Argument Extraction via Optimal Transport (2022.findings-acl)
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| Challenge: | Prior work on event-level EAE models ignore syntactic structures for documents . prior work on EE is restricted to sentence-level setting where event triggers and arguments are assumed to appear in the same sentences. |
| Approach: | They propose to employ Optimal Transport to induce structures of documents based on sentence-level syntactic structures and tailored to EAE task. |
| Outcome: | The proposed model is effective in document-level EAE, with a new constraint on unrelated context words. |
N-Shot Learning for Augmenting Task-Oriented Dialogue State Tracking (2022.findings-acl)
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| Challenge: | augmentation of task-oriented dialogues has followed standard methods for plain-text despite its richly annotated structure. |
| Approach: | They propose an augmentation framework that utilizes belief state annotations to match turns from various dialogues and form new synthetic dialogues in a bottom-up manner. |
| Outcome: | The proposed framework performs better on seen values and more robust to unseen values on n-shot training scenarios. |
Document-Level Relation Extraction with Adaptive Focal Loss and Knowledge Distillation (2022.findings-acl)
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| Challenge: | Document-level relation extraction (DocRE) is a more challenging task than sentence-level one. |
| Approach: | They propose a semi-supervised framework for document-level relation extraction with three components . they use an axial attention module for learning the interdependency among entity-pairs . |
| Outcome: | The proposed model outperforms baseline models on two DocRE datasets and outperformed previous models on human annotated data and distantly supervised data. |
Calibration of Machine Reading Systems at Scale (2022.findings-acl)
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| Challenge: | Existing methods to calibrate open setting machine reading systems fail to scale to these settings due to various scale limitations in practical settings. |
| Approach: | They propose to extend existing calibration approaches to calibrate open-domain question answering and claim verification systems to these settings. |
| Outcome: | The proposed calibration methods can selectively predict answers when question answering systems are posed with unanswerable or out-of-the-training distribution questions. |
Towards Adversarially Robust Text Classifiers by Learning to Reweight Clean Examples (2022.findings-acl)
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| Challenge: | Existing defense methods improve the adversarial robustness by making models adapt to training set augmented with some adversarials. |
| Approach: | They propose to introduce a reweighting mechanism to calibrate the training distribution to obtain robust models. |
| Outcome: | The proposed method minimizes the loss of validation set mixed with clean examples and adversarial ones in an online learning manner. |
Morphosyntactic Tagging with Pre-trained Language Models for Arabic and its Dialects (2022.findings-acl)
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| Challenge: | Pre-trained morphosyntactic tagging models outperform existing systems in Modern Standard Arabic and all the Arabic dialects studied. |
| Approach: | They present results on morphosyntactic tagging across different varieties of Arabic using pre-trained transformer language models. |
| Outcome: | The proposed models outperform existing systems in Modern Standard Arabic, 2.8% in Gulf, 1.6% in Egyptian, and 8.3% in Levantine. |
How Pre-trained Language Models Capture Factual Knowledge? A Causal-Inspired Analysis (2022.findings-acl)
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Shaobo Li, Xiaoguang Li, Lifeng Shang, Zhenhua Dong, Chengjie Sun, Bingquan Liu, Zhenzhou Ji, Xin Jiang, Qun Liu
| Challenge: | Recent studies show that pre-trained language models can fill in the missing factual words in cloze-style prompts such as ”Dante was born in [MASK]” . |
| Approach: | They propose to quantitatively measure and evaluate the word-level patterns that PLMs depend on to generate the missing factual words. |
| Outcome: | The proposed model fills in the missing factual words in cloze-style prompts by relying on effective clues or shortcut patterns. |
Metadata Shaping: A Simple Approach for Knowledge-Enhanced Language Models (2022.findings-acl)
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| Challenge: | Existing methods to capture entity knowledge with factual knowledge are limited . despite its simplicity, metadata shaping is quite effective . |
| Approach: | They propose a method which inserts substrings corresponding to readily available entity metadata into examples at train and inference time based on mutual information. |
| Outcome: | The proposed method exceeds the baseline model by 4.3 F1 points and achieves state-of-the-art results. |
Enhancing Natural Language Representation with Large-Scale Out-of-Domain Commonsense (2022.findings-acl)
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| Challenge: | Using commonsense in text understanding tasks can cause catastrophic forgetting due to domain discrepancy . previous methods of using textual descriptions as extra input information cannot apply to large-scale commonsensing. |
| Approach: | They propose to use out-of-domain commonsense to enhance text representation . they propose to integrate commonsensense descriptions into large-scale models . |
| Outcome: | The proposed model can integrate commonsense descriptions and enhance them to the target text representation without pre-training on large-scale unsupervised corpora. |
Weighted self Distillation for Chinese word segmentation (2022.findings-acl)
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| Challenge: | Recent researches show that multi-criteria resources and n-gram features are beneficial to Chinese word segmentation (CWS). |
| Approach: | They propose a framework that uses weighted self distillation to learn Chinese word segmentation using unigram features. |
| Outcome: | The proposed framework achieves state-of-the-art or competitive performance on SIGHAN Bakeoff datasets. |
Sibylvariant Transformations for Robust Text Classification (2022.findings-acl)
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| Challenge: | Existing text transformation techniques are limited in their ability to expand input space . many techniques can artificially expand labeled training sets or test suites, but are class-preserving . |
| Approach: | They propose a concept of sibylvariance to describe transforms that relax the label-preserving constraint and knowably vary the expected class. |
| Outcome: | The proposed transforms can expand input space, but they are limited in their ability to expand . the proposed transform can knowably vary the expected class and lead to more diverse distributions . |
DaLC: Domain Adaptation Learning Curve Prediction for Neural Machine Translation (2022.findings-acl)
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| Challenge: | Current research in NMT Domain Adaptation rarely provides insights on the amount of data required to perform Domain . |
| Approach: | They propose a Domain adaptation learning curve prediction model that predicts prospective DA performance based on in-domain monolingual samples in the source language. |
| Outcome: | The proposed model predicts prospective DA performance based on in-domain monolingual samples in the source language. |
Hey AI, Can You Solve Complex Tasks by Talking to Agents? (2022.findings-acl)
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| Challenge: | CommaQA is a benchmark for learning to solve complex tasks by communicating with existing agents in natural language. |
| Approach: | They propose a synthetic benchmark with three complex reasoning tasks designed to be solved by communicating with existing QA agents. |
| Outcome: | The proposed model outperforms models that learn to communicate with agents without auxiliary supervision or data. |
Modality-specific Learning Rates for Effective Multimodal Additive Late-fusion (2022.findings-acl)
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| Challenge: | Multimodal machine learning uses additive late-fusion to combine feature representations from different modalities into a joint representation. |
| Approach: | They propose a Modality-Specific Learning Rate method to build late-fusion multimodal models from fine-tuned unimodal models. |
| Outcome: | The proposed method outperforms global learning rates on multiple tasks and settings and enables the models to effectively learn each modality. |
BiSyn-GAT+: Bi-Syntax Aware Graph Attention Network for Aspect-based Sentiment Analysis (2022.findings-acl)
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| Challenge: | Aspect-based sentiment analysis is challenging because a sentence may contain multiple aspects or complicated relationships. |
| Approach: | They propose a bi-syntax aware Graph Attention Network to model the context of every aspect and sentiment relations across aspects for learning. |
| Outcome: | The proposed model outperforms the state-of-the-art methods on four benchmark datasets. |
IndicBART: A Pre-trained Model for Indic Natural Language Generation (2022.findings-acl)
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| Challenge: | IndicBART is a multilingual, sequence-to-sequence pre-trained model focusing on 11 Indic languages and English. |
| Approach: | They present a multilingual sequence-to-sequence pre-trained model for Indic languages . they evaluate it on two NLG tasks: Neural Machine Translation and extreme summarization . |
| Outcome: | The proposed model performs well on low-resource translation scenarios . Script sharing, multilingual training, and better utilization contribute to the performance. |
Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models (2022.findings-acl)
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| Challenge: | Sentence embeddings are useful for language processing tasks, but it is unclear how to produce them from encoder-decoder models. |
| Approach: | They investigate the effects of scaling up sentence encoders to 11B parameters on sentence embeddings from text-to-text transformers (T5) . |
| Outcome: | The proposed models outperform the previous best models on both SentEval and SentGLUE transfer tasks. |
Improving Relation Extraction through Syntax-induced Pre-training with Dependency Masking (2022.findings-acl)
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| Challenge: | Existing studies require modifications to existing baseline architectures to leverage syntactic information. |
| Approach: | They propose to leverage syntactic information to improve relation extraction by training a syntax-induced encoder on auto-parsed data through dependency masking. |
| Outcome: | The proposed approach outperforms baseline models and achieves state-of-the-art results on two English datasets. |
Striking a Balance: Alleviating Inconsistency in Pre-trained Models for Symmetric Classification Tasks (2022.findings-acl)
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| Challenge: | Inconsistency is observed in symmetric classification tasks that take two inputs and require the output to be invariant of the order of the inputs. |
| Approach: | They propose a consistency loss function to alleviate inconsistency in symmetric classification tasks that take two inputs and require the output to be invariant of the order of the inputs. |
| Outcome: | The proposed model improves consistency in predictions for three paraphrase detection datasets without significant drop in accuracy scores. |
Diversifying Content Generation for Commonsense Reasoning with Mixture of Knowledge Graph Experts (2022.findings-acl)
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| Challenge: | Recent years have seen a surge of interest in improving the generation quality of commonsense reasoning tasks. |
| Approach: | They propose a method that diversifies the generative reasoning by a mixture of expert strategy on commonsense knowledge graphs to encourage various generation outputs. |
| Outcome: | The proposed method improves diversity while achieving on par performance on two GCR benchmarks, based on both automatic and human evaluations. |
Dict-BERT: Enhancing Language Model Pre-training with Dictionary (2022.findings-acl)
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Wenhao Yu, Chenguang Zhu, Yuwei Fang, Donghan Yu, Shuohang Wang, Yichong Xu, Michael Zeng, Meng Jiang
| Challenge: | Pre-trained language models (PLMs) capture word semantics in different contexts, hence the embeddings of rare words on the tail are poorly optimized. |
| Approach: | They propose to leverage definitions of rare words in dictionaries to enhance language model pre-training by leveraging dictionary definitions. |
| Outcome: | The proposed model improves understanding of rare words and boosts performance on various NLP downstream tasks. |
A Feasibility Study of Answer-Agnostic Question Generation for Education (2022.findings-acl)
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Liam Dugan, Eleni Miltsakaki, Shriyash Upadhyay, Etan Ginsberg, Hannah Gonzalez, DaHyeon Choi, Chuning Yuan, Chris Callison-Burch
| Challenge: | a feasibility study into the applicability of answer-agnostic question generation models to textbook passages is conducted . a significant portion of errors arise from asking irrelevant or un-interpretable questions, a study finds . |
| Approach: | They conduct a feasibility study into the applicability of answer-agnostic question generation models to textbook passages. |
| Outcome: | The proposed model reduces the time it takes to write questions that target salient concepts . the proposed model would help professors write quizzes faster and help students stay engaged . |
Relevant CommonSense Subgraphs for “What if...” Procedural Reasoning (2022.findings-acl)
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| Challenge: | Existing knowledge graphs and commonsense are used to learn causal reasoning over procedural text. |
| Approach: | They propose a multi-hop graph reasoning model to efficiently extract a commonsense subgraph with the most relevant information from a large knowledge graph and predict the causal answer by reasoning over the representations obtained from the commonsen subgraph and contextual interactions between the questions and context. |
| Outcome: | The proposed model achieves state-of-the-art on WIQA benchmark and is comparable to previous models. |
Combining Feature and Instance Attribution to Detect Artifacts (2022.findings-acl)
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| Challenge: | In this paper, we evaluate use of different attribution methods for aiding identification of training data artifacts. |
| Approach: | They propose hybrid methods that combine saliency maps and instance attribution methods to aid in identifying training data artifacts. |
| Outcome: | The proposed methods can be used to efficiently uncover artifacts in training data when a challenging validation set is available. |
Leveraging Expert Guided Adversarial Augmentation For Improving Generalization in Named Entity Recognition (2022.findings-acl)
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| Challenge: | Named Entity Recognition (NER) systems perform well on in-distribution data, but perform poorly on examples drawn from a shifted distribution. |
| Approach: | They propose to use expert-guided heuristics to change entity tokens and their contexts to alter their entity types as adversarial attacks. |
| Outcome: | The proposed model significantly improves performance on the challenging set and out-of-domain generalization. |
Label Semantics for Few Shot Named Entity Recognition (2022.findings-acl)
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Jie Ma, Miguel Ballesteros, Srikanth Doss, Rishita Anubhai, Sunil Mallya, Yaser Al-Onaizan, Dan Roth
| Challenge: | Named entity recognition (NER) is a fundamental natural language understanding task that requires large amounts of high quality annotated in-domain data. |
| Approach: | They propose a neural architecture that leverages the semantic information in the names of the labels to give the model additional signal and enriched priors. |
| Outcome: | The proposed model is especially effective in low resource settings. |
Detection, Disambiguation, Re-ranking: Autoregressive Entity Linking as a Multi-Task Problem (2022.findings-acl)
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| Challenge: | Existing methods for entity linking do not use a knowledge base or candidate sets. |
| Approach: | They propose an autoregressive entity linking model that is trained with two auxiliary tasks and learns to re-rank generated samples at inference time. |
| Outcome: | The proposed model improves on two biomedical datasets and a news domain dataset without the use of a knowledge base or candidate sets. |
VISITRON: Visual Semantics-Aligned Interactively Trained Object-Navigator (2022.findings-acl)
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Ayush Shrivastava, Karthik Gopalakrishnan, Yang Liu, Robinson Piramuthu, Gokhan Tur, Devi Parikh, Dilek Hakkani-Tur
| Challenge: | Interactive robots navigating photo-realistic environments need to be trained to handle dynamic nature of dialogue and vision-and-language navigation (VLN). |
| Approach: | They propose a Transformer-based multi-modal navigator that is better suited to the interactive regime inherent to Cooperative Vision-and-Dialog Navigation (CVDN). |
| Outcome: | The proposed model is trained to identify and associate object-level concepts and semantics between the environment and dialogue history and identify when to interact vs. navigate via imitation learning of a binary classification head. |
Investigating Selective Prediction Approaches Across Several Tasks in IID, OOD, and Adversarial Settings (2022.findings-acl)
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| Challenge: | Existing approaches to selective prediction are not consistently outperform the simplest baseline MaxProb in all three settings. |
| Approach: | They propose to use a large-scale setup of 17 datasets to study selective prediction in NLP tasks using in-domain, out-of-domain and adversarial settings. |
| Outcome: | The proposed approaches outperform the simplest baseline MaxProb in in-domain, out-of-domain and adversarial settings, but none consistently outperformed in all three settings. |
Unsupervised Natural Language Inference Using PHL Triplet Generation (2022.findings-acl)
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| Challenge: | In some cases, training samples may not be available or collecting them could be time-consuming and resource-intensive. |
| Approach: | They propose a procedural approach that leverages sentence transformations to collect PHL triplets for training NLI models. |
| Outcome: | The proposed model outperforms existing models on several NLI benchmarks with a set of sentence transformations. |
Data Augmentation and Learned Layer Aggregation for Improved Multilingual Language Understanding in Dialogue (2022.findings-acl)
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| Challenge: | Multi-SentAugment and LayerAgg are self-training methods that augment available training data with similar (automatically labelled) in-domain sentences from large monolingual Web-scale corpora. |
| Approach: | They propose to use multi-sentaugment and layeragg to improve dialogue natural language understanding across multiple languages. |
| Outcome: | The proposed methods generalise well in zero- and few-shot scenarios and leverage external unannotated data sources. |
Ranking-Constrained Learning with Rationales for Text Classification (2022.findings-acl)
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| Challenge: | Existing approaches to text classification use labels and rationales as ranking constraints. |
| Approach: | They propose a ranking-constrained loss function that combines cross-entropy loss with ranking losses as rationale constraints to speed up deep learning models with limited training data. |
| Outcome: | The proposed approach outperforms baselines on three human-annotated datasets and shows that it is more efficient than existing approaches. |
CaM-Gen: Causally Aware Metric-Guided Text Generation (2022.findings-acl)
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| Challenge: | Content is created for a well-defined purpose, often described by a metric or signal . external metrics and content tend to have inherent relationships and not all of them may be of consequence. |
| Approach: | They propose a mechanism to guide generative models by user-defined target metrics . authors propose generative networks guided by causally significant aspects of text . |
| Outcome: | The proposed models beat baselines in terms of the target metric control while maintaining fluency and language quality of the generated text. |
Training Dynamics for Text Summarization Models (2022.findings-acl)
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| Challenge: | Pre-trained language models have shown impressive results when fine-tuned on large summarization datasets. |
| Approach: | They analyze the training dynamics for generation models, focusing on summarization . they find that a propensity to copy the input is learned early in the training process . |
| Outcome: | The proposed model learns at different stages of fine-tuning, the authors show . they show that factual errors are learnt in later stages, but not at high-loss tokens . |
Richer Countries and Richer Representations (2022.findings-acl)
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| Challenge: | Using BERT, countries with low frequency in training data are less likely to be invocabulary, and are less frequently predicted in the masked language modeling task. |
| Approach: | They propose three criteria to characterize the quality of representations for particular entities or groups: consistency, distinctiveness, and recognizability. |
| Outcome: | The results suggest that frequency is highly correlated with a country’s GDP, perpetuating historic power and wealth inequalities. |
BBQ: A hand-built bias benchmark for question answering (2022.findings-acl)
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Alicia Parrish, Angelica Chen, Nikita Nangia, Vishakh Padmakumar, Jason Phang, Jana Thompson, Phu Mon Htut, Samuel Bowman
| Challenge: | NLP models learn social biases, but little work has been done on how these biase manifest in outputs for applied tasks like question answering (QA). |
| Approach: | They propose a dataset that highlights attested social biases against people belonging to protected classes along nine social dimensions relevant for U.S. English-speaking contexts. |
| Outcome: | The proposed dataset highlights attested social biases against people belonging to protected classes along nine social dimensions relevant for U.S. English-speaking contexts. |
Zero-shot Learning for Grapheme to Phoneme Conversion with Language Ensemble (2022.findings-acl)
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| Challenge: | Existing work focuses on low-resource and endangered languages with limited training sets. |
| Approach: | They propose a hypothesis set for any unseen target language and combine it with a confusion network to propose 'the most likely hypothesis' they test the approach on over 600 unseened languages and demonstrate it significantly outperforms baselines. |
| Outcome: | The proposed model outperforms baselines on over 600 unseen languages. |
Dim Wihl Gat Tun: The Case for Linguistic Expertise in NLP for Under-Documented Languages (2022.findings-acl)
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Clarissa Forbes, Farhan Samir, Bruce Oliver, Changbing Yang, Edith Coates, Garrett Nicolai, Miikka Silfverberg
| Challenge: | Recent progress in NLP is driven by pretrained models leveraging massive datasets. |
| Approach: | They argue that IGT data can be leveraged provided target language expertise is available and that it can be used to create effective models. |
| Outcome: | The proposed model can be leveraged provided that target language expertise is available. |
Question Generation for Reading Comprehension Assessment by Modeling How and What to Ask (2022.findings-acl)
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| Challenge: | Existing Question Generation systems focus on extractive questions and do not control the type of questions. |
| Approach: | They propose a question generation model that generates inferential questions from text . they propose he model can generate questions annotated with story-based reading comprehension skills . |
| Outcome: | The proposed model outperforms baselines on a reading comprehension dataset. |
TABi: Type-Aware Bi-Encoders for Open-Domain Entity Retrieval (2022.findings-acl)
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| Challenge: | Existing methods for entity retrieval use mention boundaries but unstructured text . state-of-the-art methods struggle to retrieve rare entities for ambiguous mentions . |
| Approach: | They propose a method to jointly train bi-encoders on knowledge graph types and unstructured text for entity retrieval for open-domain tasks. |
| Outcome: | The proposed method improves retrieval of rare entities on Ambiguous Entity Retrieval sets while maintaining strong overall retrieval performance on open-domain tasks. |
Hierarchical Recurrent Aggregative Generation for Few-Shot NLG (2022.findings-acl)
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| Challenge: | Existing approaches do not account for the fact that some sub-tasks, specifically aggregation and lexicalisation, can benefit from transfer learning in different extents. |
| Approach: | They propose a hierarchical approach for few-shot and zero-shot generation using a three-moduled jointly trained architecture. |
| Outcome: | The proposed approach achieves state-of-the-art on few-shot and zero-shot settings compared to previous approaches. |
Training Text-to-Text Transformers with Privacy Guarantees (2022.findings-acl)
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| Challenge: | Recent advances in NLP often stem from large transformer-based pre-trained models. |
| Approach: | They propose differentially private (DP) training as a potential mitigation for models that can memorize parts of training data. |
| Outcome: | The proposed model can memorize parts of training data and mitigate memorization concerns. |
Revisiting Uncertainty-based Query Strategies for Active Learning with Transformers (2022.findings-acl)
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| Challenge: | a recent study has investigated how transformer-based language models can be combined with active learning. |
| Approach: | They propose to combine transformer-based language models with active learning to reduce labeling costs . transformers are expensive, but they can be fine-tuned using a query strategy . they compare transformers to experiments from previous research to evaluate their performance . |
| Outcome: | The proposed model outperforms the well-known prediction entropy query strategy on five widely used text classification benchmarks. |
The impact of lexical and grammatical processing on generating code from natural language (2022.findings-acl)
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| Challenge: | Yin and Neubig (2018) identify four key components of importance for natural language to code translation. |
| Approach: | They propose a seq2seq-based architecture that relies on a grammar-based decoder and a lexical substitution component for natural language to code translation. |
| Outcome: | The proposed architecture relies on a grammar-based decoder and a BERT encoder . the proposed architecture is based on lexical substitutions in natural language to code translation . |
Seq2Path: Generating Sentiment Tuples as Paths of a Tree (2022.findings-acl)
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| Challenge: | Existing generative methods for extracting sentiment tuples do not have orders between the t-uples . a novel parallel generative framework for ABSA is proposed . |
| Approach: | They propose a parallel generative framework to generate sentiment tuples as paths of a tree . they train the model with an independent target and introduce a discriminative token . |
| Outcome: | The proposed method achieves state-of-the-art on AOPE, ASTE, TASD, UABSA, ACOS . it trains with the loss of ordinary Seq2Seq averaged over paths, and inferences automatically select valid paths. |
Mitigating the Inconsistency Between Word Saliency and Model Confidence with Pathological Contrastive Training (2022.findings-acl)
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| Challenge: | Neural networks are used for various NLP tasks, but their complexity makes them difficult to interpret. |
| Approach: | They propose a framework to mitigate the model pathology and obtain more interpretable models by using contrastive learning and saliency-based samples augmentation to calibrate the sentences representation. |
| Outcome: | The proposed framework can mitigate the model pathology and generate more interpretable models while keeping the model performance. |
Your fairness may vary: Pretrained language model fairness in toxic text classification (2022.findings-acl)
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| Challenge: | Pre-trained, bidirectional language models have revolutionized natural language processing research . authors show that focusing on accuracy measures alone can lead to models with wide variation in fairness characteristics . |
| Approach: | They propose to use two post-processing methods to improve model fairness without retraining . they use pretrained language models of varying sizes on two toxic text classification tasks . |
| Outcome: | The proposed methods improve model fairness without retraining . the results show that the fairness variation is more than just accuracy . |
ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning (2022.findings-acl)
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| Challenge: | Existing datasets that focus on complex reasoning questions do not address such questions as they are template-based and answers come from a fixed-vocabulary. |
| Approach: | They propose a large-scale benchmark that uses visual and logical reasoning to answer questions using a transformer-based model. |
| Outcome: | The proposed models achieve state-of-the-art on the previous datasets and on the current one, but also show that they have several challenges in answering complex reasoning questions. |
A Novel Perspective to Look At Attention: Bi-level Attention-based Explainable Topic Modeling for News Classification (2022.findings-acl)
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| Challenge: | Existing deep learning models have the attention mechanism to improve performance, but the inherent characteristics of deep learning model complexity and the flexibility of the attention structure make them difficult to explain. |
| Approach: | They propose a two-tier attention architecture to decouple the complexity of explanation and the decision-making process by using large-scale news corpora. |
| Outcome: | The proposed model can achieve competitive performance with state-of-the-art models and illustrates its appropriateness from an explainability perspective. |
Learn and Review: Enhancing Continual Named Entity Recognition via Reviewing Synthetic Samples (2022.findings-acl)
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| Challenge: | Existing methods for named entity recognition classify mentions into fixed set of predefined entity types but in many real-world scenarios, new entity types are incrementally involved. |
| Approach: | They propose a two-stage framework Learn-and-Review for continual named entity recognition to alleviate inter-type confusion. |
| Outcome: | The proposed framework outperforms the state-of-the-art method on CoNLL-03 and OntoNotes-5.0. |
Phoneme transcription of endangered languages: an evaluation of recent ASR architectures in the single speaker scenario (2022.findings-acl)
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| Challenge: | Recent work on phonetic transcription is reported to be the bottleneck in endangered languages . however, when a single speaker is involved, small amounts of training are needed . |
| Approach: | They compare automatic speech recognition (ASR) approaches to speaker-dependent phonetic transcription using a common dataset of 11 languages. |
| Outcome: | The proposed system handles morphologically complex languages and writing systems for which no pronunciation dictionary exists. |
Does BERT really agree ? Fine-grained Analysis of Lexical Dependence on a Syntactic Task (2022.findings-acl)
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| Challenge: | lexically-independent subject-verb number agreement (NA) is performed by transformer-based neural language models (NLMs) . but when as little as one attractor is present, the model fails to perform lexical generalization . |
| Approach: | They propose to disrupt lexical patterns found in naturally occurring stimuli for each targeted structure in a novel fine-grained analysis of BERT's behavior. |
| Outcome: | The proposed model generalizes well for simple templates, but fails to perform lexically-independent generalization when as little as one attractor is present. |
Combining Static and Contextualised Multilingual Embeddings (2022.findings-acl)
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| Challenge: | Static embeddings are less expressive than contextual language models, but can be more straightforwardly aligned across multiple languages. |
| Approach: | They extract static embeddings for 40 languages from XLM-R and validate them with cross-lingual word retrieval and then align them using VecMap. |
| Outcome: | The proposed approach improves multilingual representations by leveraging static embeddings and a pre-training code. |
An Accurate Unsupervised Method for Joint Entity Alignment and Dangling Entity Detection (2022.findings-acl)
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| Challenge: | Existing methods for knowledge graph integration lack dangling entities that can be manually extracted. |
| Approach: | They propose a Unsupervised method for joint Entity alignment and Dangling entity detection that uses literal semantic information to generate pseudo entity pairs and globally guided alignment information for EA. |
| Outcome: | The proposed method outperforms state-of-the-art methods in the EA and DED tasks and achieves comparable results without supervision. |
Square One Bias in NLP: Towards a Multi-Dimensional Exploration of the Research Manifold (2022.findings-acl)
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| Challenge: | a prototypical NLP experiment trains a standard architecture on labeled English data . a recent study shows that research often goes beyond the square one setup . |
| Approach: | They argue that the prototypical NLP experiment trains a standard architecture on labeled English data and optimizes for accuracy without accounting for other dimensions such as fairness, interpretability, or computational efficiency. |
| Outcome: | The proposed model steers and biases the research dynamics in the NLP community, the authors argue . they show that the prototype biased recent NLP research on English data is true . |
Systematicity, Compositionality and Transitivity of Deep NLP Models: a Metamorphic Testing Perspective (2022.findings-acl)
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| Challenge: | Existing studies focus on robustness-like metamorphic relations, which limit the scope of linguistic properties they can test. |
| Approach: | They propose three new classes of metamorphic relations which address the properties of systematicity, compositionality and transitivity. |
| Outcome: | The proposed methods show that metamorphic models do not always behave according to expected linguistic properties. |
Improving Neural Political Statement Classification with Class Hierarchical Information (2022.findings-acl)
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Erenay Dayanik, Andre Blessing, Nico Blokker, Sebastian Haunss, Jonas Kuhn, Gabriella Lapesa, Sebastian Pado
| Challenge: | skewed classification of fine-grained categories in text-based computational social science is challenging on the NLP side. |
| Approach: | They propose to use hierarchical relations among categories in codebooks to create constraints on the learned model. |
| Outcome: | The proposed model improves on two datasets and multiple languages. |
Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation (2022.findings-acl)
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| Challenge: | Recent large-scale vision-language pre-training models are powerful in multimodal classification and retrieval tasks. |
| Approach: | They propose to augment a vision-language pre-training model with a textual pre-trained language model . the model achieves 44.5% zero-shot accuracy on multimodal generation tasks . |
| Outcome: | The proposed model achieves 44.5% zero-shot accuracy on open-ended visual question answering and image captioning tasks. |
Co-VQA : Answering by Interactive Sub Question Sequence (2022.findings-acl)
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| Challenge: | Existing approaches to Visual Question Answering (VQA) answer questions directly, but people usually decompose a complex question into a sequence of simple sub questions. |
| Approach: | They propose a conversation-based VQA framework that decomposes questions into sub questions and answers them one-by-one. |
| Outcome: | The proposed framework achieves state-of-the-art on VQA 2.0 and VQA-CP v2 datasets. |
A Simple Hash-Based Early Exiting Approach For Language Understanding and Generation (2022.findings-acl)
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Tianxiang Sun, Xiangyang Liu, Wei Zhu, Zhichao Geng, Lingling Wu, Yilong He, Yuan Ni, Guotong Xie, Xuanjing Huang, Xipeng Qiu
| Challenge: | Existing methods to measure instance difficulty use generalization and threshold-tuning . a new approach to learn to exit is based on hash functions to assign tokens to a fixed exiting layer. |
| Approach: | They propose a Hash-based Early Exiting approach that replaces learn-to-exit modules with hash functions to assign each token to a fixed exiting layer. |
| Outcome: | The proposed approach improves on learning to exit and predicting instance difficulty. |
Auxiliary tasks to boost Biaffine Semantic Dependency Parsing (2022.findings-acl)
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| Challenge: | Semantic dependency parsing (SDP) is a task of producing a dependency graph for a sentence. |
| Approach: | They propose to use simple auxiliary tasks that introduce some form of interdependence between arcs to circumvent such an independence of decision. |
| Outcome: | The proposed method shows modest but systematic performance gains on a near-state-of-the-art baseline using transformer-based contextualized representations. |
Syntax-guided Contrastive Learning for Pre-trained Language Model (2022.findings-acl)
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| Challenge: | Existing studies rely on additional syntax-driven attention components to enhance the transformer, which require more parameters and additional syntactic parsing in downstream tasks. |
| Approach: | They propose a syntax-guided contrastive learning method which does not change the transformer architecture and does not alter the transformer structure. |
| Outcome: | The proposed method achieves consistent improvements in a variety of tasks including grammatical error detection, entity tasks, structural probing and GLUE. |
Improved Multi-label Classification under Temporal Concept Drift: Rethinking Group-Robust Algorithms in a Label-Wise Setting (2022.findings-acl)
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| Challenge: | Large-scale multi-label document classification presents interesting challenges due to the large label space and two-tiered skewed label distributions. |
| Approach: | They evaluate several group-robust optimization algorithms proposed to mitigate temporal concept drift and class imbalance in document classification. |
| Outcome: | The proposed algorithms outperform sampling-based approaches to class imbalance and concept drift and lead to much better performance on minority classes. |
ASCM: An Answer Space Clustered Prompting Method without Answer Engineering (2022.findings-acl)
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| Challenge: | Pre-trained language models have shown a great impact on NLP tasks. |
| Approach: | They propose an answer space clustered prompting model and a synonym initialization method that automatically categorizes all answer tokens in a semantic-clustered embedding space. |
| Outcome: | Experiments show that the proposed method outperforms existing state-of-the-art methods in few-shot settings. |
Why don’t people use character-level machine translation? (2022.findings-acl)
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| Challenge: | despite evidence character-level systems are comparable with subword systems, they are rarely used in competitive setups in machine translation competitions. |
| Approach: | They propose a two-step decoder architecture that does not suffer from a slow-down due to the length of character sequences. |
| Outcome: | The proposed character-level MT systems show better domain robustness and better morphological generalization . the proposed decoder architecture shows no slow-down due to the length of character sequences . |
Seeking Patterns, Not just Memorizing Procedures: Contrastive Learning for Solving Math Word Problems (2022.findings-acl)
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| Challenge: | Existing models memorize procedures from context and rely on shallow heuristics to solve MWPs. |
| Approach: | They propose a contrastive learning approach where the neural network perceives the divergence of patterns. |
| Outcome: | The proposed method greatly improves performance in monolingual and multilingual settings. |
xGQA: Cross-Lingual Visual Question Answering (2022.findings-acl)
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Jonas Pfeiffer, Gregor Geigle, Aishwarya Kamath, Jan-Martin Steitz, Stefan Roth, Ivan Vulić, Iryna Gurevych
| Challenge: | a lack of multilingual multimodal datasets has hindered multimodal vision and language modeling efforts. |
| Approach: | They propose a multilingual evaluation benchmark for the visual question answering task . they extend the established English GQA dataset to 7 typologically diverse languages . |
| Outcome: | The proposed methods outperform current state-of-the-art models in zero-shot cross-lingual settings, but the accuracy remains low across languages. |
Automatic Speech Recognition and Query By Example for Creole Languages Documentation (2022.findings-acl)
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| Challenge: | CREAM project aims to provide linguists with new methods for language documentation based on automatic speech recognition and keyword-spotting. |
| Approach: | They propose to use one hour of annotated data to design an automatic speech recognition system for two Creole languages. |
| Outcome: | The proposed model is based on an hour of annotated data and is usable by linguists. |
MReD: A Meta-Review Dataset for Structure-Controllable Text Generation (2022.findings-acl)
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| Challenge: | a new text generation dataset is needed to controllable text summarization, but it lacks the domain knowledge. |
| Approach: | They propose to use existing text generation datasets to leverage input and control signals . they propose to annotate each meta-review sentence manually with a control signal . |
| Outcome: | The proposed method can be used to control the structure of a text generation dataset . it can be applied to a variety of tasks, including a task with a large number of meta-review sentences . |
Single Model Ensemble for Subword Regularized Models in Low-Resource Machine Translation (2022.findings-acl)
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| Challenge: | Existing subword regularizations use multiple segmentations during training but only use one segmentation in inference. |
| Approach: | They propose an inference strategy that uses multiple subword segmentations to solve this discrepancy in the training process and inference. |
| Outcome: | The proposed strategy reduces the cost of training and improves the performance of models trained with subword regularization in low-resource machine translation tasks. |
Detecting Various Types of Noise for Neural Machine Translation (2022.findings-acl)
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| Challenge: | a recent study investigated the impact of noise on the performance of machine translation systems. |
| Approach: | They propose to combine recent research on data filtering with original analysis . they find that most of the suggested noise types can be detected with 90% accuracy . |
| Outcome: | The proposed filtering systems can detect noise types with 90% accuracy in high resource settings. |
DU-VLG: Unifying Vision-and-Language Generation via Dual Sequence-to-Sequence Pre-training (2022.findings-acl)
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| Challenge: | Existing vision-and-language generation models cannot utilize pair-wise images and text through bi-directional generation due to the limitations of the model structure and pre-training objectives. |
| Approach: | They propose a framework which unifies vision-and-language generation as sequence generation problems. |
| Outcome: | The proposed framework achieves better performance than variants trained with uni-directional generation objectives or the variant without the commitment loss on image captioning and text-to-image generation datasets. |
HiCLRE: A Hierarchical Contrastive Learning Framework for Distantly Supervised Relation Extraction (2022.findings-acl)
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| Challenge: | Existing approaches of distantly supervised relation extraction (DSRE) focus on sentence-level or bag-level de-noising, neglecting the explicit interaction with cross levels. |
| Approach: | They propose a hierarchical contrastive learning framework for distantly supervised relation extraction to reduce noisy sentences. |
| Outcome: | The proposed framework outperforms baselines in various mainstream DSRE datasets. |
Prompt-Driven Neural Machine Translation (2022.findings-acl)
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| Challenge: | Neural machine translation models still face various challenges including fragility and lack of style flexibility. |
| Approach: | They propose to incorporate prompts into neural machine translation to improve translation control and style flexibility. |
| Outcome: | Empirical results show that the proposed method improves translation control and quality and improves human-in-the-loop translation. |
On Controlling Fallback Responses for Grounded Dialogue Generation (2022.findings-acl)
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| Challenge: | Existing knowledge grounded dialogue frameworks assume that the user intention is always answerable. |
| Approach: | They propose a framework that automatically generates a control token with the generator to bias the succeeding response towards informativeness for answerable contexts and fallback for unanswerable context. |
| Outcome: | The proposed framework incorporates fallback responses to respond to unanswerable contexts in an informative manner while retaining informativeness for answerable context. |
CRAFT: A Benchmark for Causal Reasoning About Forces and inTeractions (2022.findings-acl)
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Tayfun Ates, M. Ateşoğlu, Çağatay Yiğit, Ilker Kesen, Mert Kobas, Erkut Erdem, Aykut Erdem, Tilbe Goksun, Deniz Yuret
| Challenge: | Existing models with similar physical and causal understanding capabilities are still underdeveloped. |
| Approach: | They propose a video question answering dataset that requires causal reasoning about physical forces and object interactions. |
| Outcome: | The proposed dataset requires causal reasoning about physical forces and object interactions. |
A Graph Enhanced BERT Model for Event Prediction (2022.findings-acl)
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| Challenge: | Existing methods to predict subsequent events use sparsity of event graph to improve performance. |
| Approach: | They propose to automatically build event graph using a BERT model by adding a structured variable to the model to learn to predict event connections. |
| Outcome: | The proposed model outperforms state-of-the-art models on two event prediction tasks. |
Long Time No See! Open-Domain Conversation with Long-Term Persona Memory (2022.findings-acl)
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| Challenge: | Existing persona dialogue datasets and models can build long-term relationships with humans . however, current open-domain dialogue systems cannot build long relationships with users . |
| Approach: | They propose a long-term memory conversation dataset and a dialogue generation framework with long-Term memory mechanism to extract and continuously update long-time persona memory. |
| Outcome: | The proposed system outperforms baselines in terms of long-term dialogue consistency . the proposed system can build long-lasting relationships between humans and bots . |
Lacking the Embedding of a Word? Look it up into a Traditional Dictionary (2022.findings-acl)
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Elena Sofia Ruzzetti, Leonardo Ranaldi, Michele Mastromattei, Francesca Fallucchi, Noemi Scarpato, Fabio Massimo Zanzotto
| Challenge: | Word embeddings are powerful dictionaries, but they fail to give sense to rare words . a large body of research is devoted to devising ways to capture word meaning . |
| Approach: | They propose to use definitions retrieved from traditional dictionaries to build word embeddings for rare words. |
| Outcome: | The proposed methods outperform state-of-the-art methods for embeddings of unknown words . the proposed methods significantly outperformed the BERT method for OOV words compared to the proposed method . |
MTRec: Multi-Task Learning over BERT for News Recommendation (2022.findings-acl)
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| Challenge: | Existing news recommendation methods learn news representations solely based on news titles. Existing methods only utilize title information and neglect other valuable news information such as categories and entities. |
| Approach: | They propose a multi-task method to incorporate multi-field information into BERT, which improves its news encoding capability. |
| Outcome: | Extensive experiments on the MIND news recommendation benchmark show the proposed method is effective. |
Cross-domain Named Entity Recognition via Graph Matching (2022.findings-acl)
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| Challenge: | Empirical results show that our method outperforms a series of transfer learning, multitask learning, and few-shot learning methods due to the data scarcity in the real-world scenario. |
| Approach: | They propose to model the label relationship as a probability distribution and construct label graphs in both source and target label spaces. |
| Outcome: | Empirical results show that the proposed method outperforms transfer learning, multi-task learning, and few-shot learning methods on four datasets. |
Assessing Multilingual Fairness in Pre-trained Multimodal Representations (2022.findings-acl)
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| Challenge: | Recent pre-trained multimodal models have shown exceptional capabilities towards connecting images and natural language. |
| Approach: | They propose two new fairness notions for pre-trained multimodal models that consider language as the fairness recipient. |
| Outcome: | The proposed models can be generalized to multilingualism by cross-lingual alignment . the results show that the models are individually fair across languages . |
More Than Words: Collocation Retokenization for Latent Dirichlet Allocation Models (2022.findings-acl)
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| Challenge: | Latent Dirichlet Allocation models ingest words to discover their latent topics . but it is unclear how to achieve the best results for languages without marked word boundaries . |
| Approach: | They propose to use retokenization to merge frequent token ngrams into collocations in input to a Latent Dirichlet Allocation model. |
| Outcome: | The proposed model improves topic coherence and coherency in Chinese and Thai . the proposed model is more coherent and clearer than unmerged models . |
Generalized but not Robust? Comparing the Effects of Data Modification Methods on Out-of-Domain Generalization and Adversarial Robustness (2022.findings-acl)
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| Challenge: | Data modification has been proposed as an effective solution for generalizing to out-of-domain (OOD) inputs. |
| Approach: | They propose to use data modification to generalize to out-of-domain inputs . they also analyze their adversarial robustness using a synthetic dataset . |
| Outcome: | The proposed data modification strategies improve OOD accuracy and AR, but data filtering hurts OOD on other tasks. |
ASSIST: Towards Label Noise-Robust Dialogue State Tracking (2022.findings-acl)
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| Challenge: | Existing versions of MultiWOZ 2.0 have been published, but there are still lots of noisy labels in the training set. |
| Approach: | They propose a framework to train dialogue state tracking models from noisy labels instead of improving annotation quality further by using auxiliary models. |
| Outcome: | The proposed framework improves the goal accuracy of DST models by 28.16% on MultiWOZ 2.0 and 8.41% on MultiWoz 2.4, compared to using only the vanilla noisy labels. |
Graph Refinement for Coreference Resolution (2022.findings-acl)
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| Challenge: | Existing models for coreference resolution are based on independent mention pair-wise decisions. |
| Approach: | They propose a model that learns coreference at the document-level and takes global decisions. |
| Outcome: | The proposed model improves over baselines, reinforcing the hypothesis that document-level information improves conference resolution. |
ECO v1: Towards Event-Centric Opinion Mining (2022.findings-acl)
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| Challenge: | Existing studies on event-centric opinion mining focus on entity-centric opinions . entity-centered opinions focus on sentimental polarity of events, while event-centered ones focus on content . |
| Approach: | They propose to perform event-centric opinion mining on event-argument structure and expression categorizing theory and benchmark it against a pioneer corpus. |
| Outcome: | The proposed task is feasible and challenging, and the results are beneficial for future studies. |
Deep Reinforcement Learning for Entity Alignment (2022.findings-acl)
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| Challenge: | Entity alignment (EA) methods identify the aligned entities based on cosine similarity, ignoring the semantics underlying the embeddings themselves. |
| Approach: | They propose to model entity alignment as a sequential decision-making task where an agent sequentially decides whether two entities are matched or mismatched based on representation vectors. |
| Outcome: | The proposed framework consistently advances the performance of several state-of-the-art methods, with a maximum improvement of 31.1% on Hits@1. |
Breaking Down Multilingual Machine Translation (2022.findings-acl)
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| Challenge: | Multilingual training is an essential ingredient in machine translation systems . but it has different effects in different multilingual settings, such as many-to-one, one-tomany and many- to-many learning . |
| Approach: | They compare multilingual training settings with encoders and decoders initialized by multilingual learning . they find important attention heads for each language pair and compare their correlations during inference . |
| Outcome: | The proposed models outperform the best models for high-resource languages and one-to-many models for low-resourced languages. |
Mitigating Contradictions in Dialogue Based on Contrastive Learning (2022.findings-acl)
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| Challenge: | Current chatbots generate fluent, informative responses but sometimes generate contradictory responses when interacting with human. |
| Approach: | They propose to use contrastive learning technique to mitigate contradiction issues in chatbots by minimizing the similarity between the target response and contradiction related negative example. |
| Outcome: | The proposed method outperforms existing methods on automatic and human evaluation while preserving response fluency. |
ELLE: Efficient Lifelong Pre-training for Emerging Data (2022.findings-acl)
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| Challenge: | Existing pre-trained language models are typically trained with static data, ignoring that streaming data of various sources may continuously grow. |
| Approach: | They propose to use function preserved model expansion to expand existing PLM's width and depth to improve efficiency of knowledge acquisition. |
| Outcome: | The proposed model improves pre-training efficiency and performance over existing models on BERT and GPT. |
EnCBP: A New Benchmark Dataset for Finer-Grained Cultural Background Prediction in English (2022.findings-acl)
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| Challenge: | Existing research on cultural background modeling is coarse-grained and does not examine cultural differences among speakers of the same language. |
| Approach: | They use a news-based cultural background prediction dataset to annotate, validate and benchmark NLP models with cultural background features. |
| Outcome: | The proposed model improves on nine syntactic, semantic, and psycholinguistic tasks while introducing cultural background information does not improve the Go-Emotions task due to text domain conflicts. |
Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models (2022.findings-acl)
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| Challenge: | Prompting language models (LMs) with training examples and task descriptions has been seen as critical to recent successes in few-shot learning. |
| Approach: | They propose to fine tune masked language models with training examples and task descriptions to reduce prompt engineering by using null prompts. |
| Outcome: | The proposed prompts can be used to improve few-shot learning by finetuning only the bias terms while updating only 0.1% of the parameters. |
uFACT: Unfaithful Alien-Corpora Training for Semantically Consistent Data-to-Text Generation (2022.findings-acl)
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| Challenge: | uFACT (Un-Faithful Alien Corpora Training) is a training corpus construction method for data-to-text generation models. |
| Approach: | They propose a training corpus construction method for data-to-text (d2t) generation models which augments a target corpus with alien corpora which have different semantic representations. |
| Outcome: | The proposed method generates utterances which represent the semantic content of the data sources more accurately compared to models trained on the target corpus alone. |
Good Night at 4 pm?! Time Expressions in Different Cultures (2022.findings-acl)
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| Challenge: | a new paper focuses on temporal grounding of time expressions to specific hours in the day . we propose language-agnostic methods for mapping time expression to specific times . cultural differences can cause variation in interpretation of time-specific expressions . |
| Approach: | They propose to use language-agnostic methods to map time expressions to specific hours . they use a time expression that is interpreted by different people . |
| Outcome: | The proposed method achieves promising results on gold standard annotations for 27 languages. |
Extracting Person Names from User Generated Text: Named-Entity Recognition for Combating Human Trafficking (2022.findings-acl)
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| Challenge: | Existing methods for Named-Entity Recognition (NER) on escort ads are not sufficient to extract person names from the text of the ad. |
| Approach: | They propose to use a model to extract person names from escort ads to capture ambiguous names and adapt to adversarial changes in the text. |
| Outcome: | The proposed model shows 19% improvement on average in the F1 classification score compared to previous state-of-the-art in two domain-specific datasets. |
OneAligner: Zero-shot Cross-lingual Transfer with One Rich-Resource Language Pair for Low-Resource Sentence Retrieval (2022.findings-acl)
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| Challenge: | a new model for parallel sentence retrieval can be used to align parallel sentences in multilingual corpora . a faithful aligner can help narrow down the candidate pool without having to deal with an enormous search space . |
| Approach: | They propose a model that can be trained on only one language pair and transfers to low-resource languages with negligible degradation in performance. |
| Outcome: | The proposed model outperforms the previous model on the Tateoba dataset by 8.0 points in accuracy and using less than 0.6% of their parallel data. |
Suum Cuique: Studying Bias in Taboo Detection with a Community Perspective (2022.findings-acl)
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| Challenge: | Prior research has shown the need to consider community language norms when studying taboo text classification and annotations. |
| Approach: | They propose to use special classifiers tuned for each community's language to study bias in taboo classification and annotation where a community perspective is front and center. |
| Outcome: | The proposed method shows that biases are strongest against African Americans and South Asians . a community perspective is front and center in the proposed method . |
Modeling Intensification for Sign Language Generation: A Computational Approach (2022.findings-acl)
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| Challenge: | End-to-end sign language generation models do not accurately represent prosody in sign language. |
| Approach: | They propose to model intensification in a data-driven manner to improve prosody in generated sign languages by modeling temporal and spatial variations. |
| Outcome: | The proposed models improve the prosody of generated sign languages by using data-driven models. |
Controllable Natural Language Generation with Contrastive Prefixes (2022.findings-acl)
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| Challenge: | Existing work on controllable natural language generation has focused on fine-tuning existing models or using attribute discriminators. |
| Approach: | They propose a lightweight framework for controllable GPT2 generation that utilizes attribute-specific vectors to steer natural language generation. |
| Outcome: | The proposed framework can guide generation towards desired attributes while keeping high linguistic quality. |
Revisiting the Effects of Leakage on Dependency Parsing (2022.findings-acl)
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| Challenge: | Recent work shows that treebank size and linguistic variation are important factors that explain the variation in dependency parsing performance. |
| Approach: | They propose a measure of leakage that explains and correlates with observed performance variation. |
| Outcome: | The proposed measure explains and correlates with observed performance variation. |
Learning to Describe Solutions for Bug Reports Based on Developer Discussions (2022.findings-acl)
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| Challenge: | Software bugs in open-source projects are reported through issue tracking systems like GitHub Issues. |
| Approach: | They propose a method for generating a natural language description of a bug by synthesizing relevant content within the discussion. |
| Outcome: | The proposed system generates a natural language description of the solution by synthesizing relevant content within the discussion. |
Perturbations in the Wild: Leveraging Human-Written Text Perturbations for Realistic Adversarial Attack and Defense (2022.findings-acl)
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| Challenge: | ANTHRO extracts over 600K human-written text perturbations and leverages them for realistic adversarial attacks. |
| Approach: | They propose an adversarial text manipulation algorithm that inductively extracts over 600K human-written text perturbations and leverages them for realistic adversarials. |
| Outcome: | The proposed algorithm outperforms the TextBugger baseline with an increase of 50% and 40% in terms of semantic preservation and stealthiness when evaluated by layperson and professional human workers. |
Improving Chinese Grammatical Error Detection via Data augmentation by Conditional Error Generation (2022.findings-acl)
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| Challenge: | Chinese Grammatical Error Detection is a non-automatic method to detect grammatical errors in texts. |
| Approach: | They propose a Conditional Non-Autoregressive Error Generation model for Chinese grammatical errors that uses a masking and prediction method to generate a context-dependent error. |
| Outcome: | The proposed method achieves better performance than all compared data augmentation methods on the CGED-2018 and CGAD-2020 benchmarks. |
Modular and Parameter-Efficient Multimodal Fusion with Prompting (2022.findings-acl)
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| Challenge: | Recent research has made impressive progress in large-scale multimodal pre-training. |
| Approach: | They propose to use prompt vectors to align multimodal modalities by pretraining text inputs with prompts or embedding vectors. |
| Outcome: | The proposed method achieves comparable performance to several other multimodal fusion methods in low-resource settings. |
Synchronous Refinement for Neural Machine Translation (2022.findings-acl)
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| Challenge: | Existing approaches to decode target sentences face a one-pass issue . generated wrong words are added to the historical context to affect the generation of subsequent target words, which hinders the performance of machine translation. |
| Approach: | They propose a synchronous refinement method to revise potential errors in the generated words by considering part of the target future context. |
| Outcome: | The proposed method can refine generated target words and generate the next target word synchronously. |
HIE-SQL: History Information Enhanced Network for Context-Dependent Text-to-SQL Semantic Parsing (2022.findings-acl)
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| Challenge: | Recent studies focus on context-dependent text-to-SQL task but fail to exploit both . et al., 2019; xu e. al.; yu y., 2021) focus on the context-independent text to SQL task . |
| Approach: | They propose a history information enhanced text-to-SQL model to exploit context dependence information from history utterances and the last predicted SQL query. |
| Outcome: | The proposed model improves performance on two context-dependent text-to-SQL benchmarks. |
CRASpell: A Contextual Typo Robust Approach to Improve Chinese Spelling Correction (2022.findings-acl)
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| Challenge: | Recent research on Chinese spelling correction methods has poor performance on multi-typo texts. |
| Approach: | They propose to use Bert-based Chinese spelling correction models to overcome these limitations by constructing a noisy context for each training sample and a copy mechanism to encourage the model to choose the input character when the miscorrected and input character are both valid. |
| Outcome: | The proposed model outperforms state-of-the-art models on widely used benchmarks and achieves a remarkable gain. |
Gaussian Multi-head Attention for Simultaneous Machine Translation (2022.findings-acl)
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| Challenge: | Existing methods for siMT do not explicitly model the alignment to perform the control. |
| Approach: | They propose to model alignment and translation in a unified manner by Gaussian Multi-head Attention (GMA) they propose to integrate alignment-related priors into the translation model to determine final attention. |
| Outcome: | The proposed method outperforms strong baselines on trade-off between translation and latency. |
Composing Structure-Aware Batches for Pairwise Sentence Classification (2022.findings-acl)
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| Challenge: | Identifying the relation between two sentences requires datasets with pairwise annotations. |
| Approach: | They propose three batch composition strategies to incorporate such information and measure their performance over 14 heterogeneous pairwise sentence classification tasks. |
| Outcome: | The proposed methods show that the pre-trained language model can benefit from having such structural information in a low-resource setting. |
Factual Consistency of Multilingual Pretrained Language Models (2022.findings-acl)
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| Challenge: | Recent work shows that monolingual English language models fill-in-the-blank differently for paraphrases describing the same fact. |
| Approach: | They propose a resource to analyze consistency of English language models . they find that mBERT is as inconsistent as English BERT in paraphrases . |
| Outcome: | The proposed model is as inconsistent as English BERT in English paraphrases, but it is more so for all the other 45 languages. |
Selecting Stickers in Open-Domain Dialogue through Multitask Learning (2022.findings-acl)
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| Challenge: | Existing methods to select appropriate stickers in open-domain dialogues have not been explored. |
| Approach: | They propose a multitask learning method consisting of three auxiliary tasks to combine multimodal information to enhance the understanding of dialogue history, emotion and semantic meaning of stickers. |
| Outcome: | The proposed model can combine multimodal information and achieve significantly higher accuracy over strong baselines. |
ZiNet: Linking Chinese Characters Spanning Three Thousand Years (2022.findings-acl)
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| Challenge: | tens of thousands of ancient characters must be deciphered by experts to interpret unearthed documents. |
| Approach: | They propose a diachronic Chinese knowledge base to help researchers discover glyph similar characters by measuring glyph similarities between ancient Chinese characters. |
| Outcome: | The proposed method shows strong correlations between the scores obtained from the method and from human experts. |
How Can Cross-lingual Knowledge Contribute Better to Fine-Grained Entity Typing? (2022.findings-acl)
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| Challenge: | Extensive experiments on multi-lingual datasets show that our method significantly outperforms multiple baselines and can robustly handle negative transfer. |
| Approach: | They propose to transfer semantic knowledge from rich-resourced languages to low-resource languages by using multilingual transfer learning. |
| Outcome: | The proposed model outperforms baselines and can handle negative transfer. |
AMR-DA: Data Augmentation by Abstract Meaning Representation (2022.findings-acl)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic representation for NLP/NLU. |
| Approach: | They propose to use AMR-DA for data augmentation in NLP . they use sentence-level techniques like back translation and token-level methods like EDA . |
| Outcome: | The proposed method outperforms EDA and AEDA and improves on STS and text classification tasks. |
Using Pre-Trained Language Models for Producing Counter Narratives Against Hate Speech: a Comparative Study (2022.findings-acl)
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| Challenge: | Autoregressive models combined with stochastic decodings are the most promising for generating CNs with regard to an unseen target of hate. |
| Approach: | They propose to use pre-trained language models to generate counter-narratives in English by adding an automatic post-editing step to refine generated CNs. |
| Outcome: | The proposed pipeline could be used to generate counter-narratives in English using pre-trained language models and stochastic decoding mechanisms. |
Improving Robustness of Language Models from a Geometry-aware Perspective (2022.findings-acl)
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| Challenge: | Recent studies have found that removing the norm-bounded projection and increasing search steps in adversarial training can significantly improve robustness. |
| Approach: | They propose friendly adversarial data augmentation and geometry-aware adversarial training to achieve stronger robustness using fewer search steps. |
| Outcome: | The proposed method can obtain stronger robustness using fewer steps than existing methods. |
Task-guided Disentangled Tuning for Pretrained Language Models (2022.findings-acl)
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| Challenge: | Pretrained language models are fine-tuned on task-specific datasets, but fail to capture task- specific patterns. |
| Approach: | They propose a method which disentangles task-relevant signals from entangled representations. |
| Outcome: | The proposed method improves generalization of representations by disentangling task-relevant signals from the entangled representations. |
Exploring the Impact of Negative Samples of Contrastive Learning: A Case Study of Sentence Embedding (2022.findings-acl)
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| Challenge: | Unsupervised contrastive learning is emerging as a powerful technique for extracting knowledge from unlabeled data. |
| Approach: | They propose a momentum contrastive learning model with negative sample queue for sentence embedding with a simulated model with EMA update mechanism. |
| Outcome: | The proposed model achieves a Spearman’s correlation of 77.27% on the semantic text similarity task and a maximum traceable distance metric. |
The Inefficiency of Language Models in Scholarly Retrieval: An Experimental Walk-through (2022.findings-acl)
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| Challenge: | Existing work does not critically analyze the scientific language models to the best of our knowledge. |
| Approach: | They evaluate scientific language models in handling short-query texts and textual neighbors by leveraging perturbations to generate textual neighbor classes. |
| Outcome: | The proposed model is ineffective for retrieving documents for short-query texts under the most relaxed conditions. |
Fusing Heterogeneous Factors with Triaffine Mechanism for Nested Named Entity Recognition (2022.findings-acl)
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| Challenge: | Named entity recognition (NER) is a fundamental natural language processing task that extracts entities from texts. |
| Approach: | They propose a triaffine mechanism which integrates heterogeneous factors into a single model to fuse these factors into one model to achieve better span representation. |
| Outcome: | The proposed method outperforms previous span-based methods and achieves state-of-the-art F1 scores on nested NER datasets GENIA and KBP2017. |
UNIMO-2: End-to-End Unified Vision-Language Grounded Learning (2022.findings-acl)
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| Challenge: | Existing methods for vision-language pre-training can only learn from aligned image-caption data and rely heavily on expensive regional features. |
| Approach: | They propose an end-to-end unified-modal pre-training framework for joint learning . they propose to conduct grounded learning on both images and texts via a sharing grounded space . |
| Outcome: | The proposed model improves visual and visual semantic alignment on images and texts. |
The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking (2022.findings-acl)
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Yinghui Li, Qingyu Zhou, Yangning Li, Zhongli Li, Ruiyang Liu, Rongyi Sun, Zizhen Wang, Chao Li, Yunbo Cao, Hai-Tao Zheng
| Challenge: | Chinese Spell Checking (CSC) aims to detect and correct spelling errors, which are caused by the phonological or visual similarity. |
| Approach: | They propose an Error-driven COntrastive Probability Optimization framework to refine the knowledge representations of pre-trained language models to avoid predicting common characters. |
| Outcome: | Extensive experiments and detailed analyses on SIGHAN datasets demonstrate that ECOPO is simple yet effective. |
XFUND: A Benchmark Dataset for Multilingual Visually Rich Form Understanding (2022.findings-acl)
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| Challenge: | Existing research on multimodal pre-training for visually rich document understanding tasks has focused on the English domain while neglecting the importance of multilingual generalization. |
| Approach: | They propose a multimodal pre-trained model for multilingual document understanding which bridges the language barriers for visually rich document understanding. |
| Outcome: | The proposed model outperforms existing cross-lingual pre-trained models on the XFUND dataset on visual document understanding tasks. |
Type-Driven Multi-Turn Corrections for Grammatical Error Correction (2022.findings-acl)
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| Challenge: | Existing studies focus on data augmentation to combat exposure bias . but data augmented models lack the ability to recognize the procedure of gradual corrections . |
| Approach: | They propose a type-driven multi-turn corrections approach that uses multiple training instances to train dominant models. |
| Outcome: | The proposed model achieves state-of-the-art single-model performance on English GEC benchmarks. |
Leveraging Knowledge in Multilingual Commonsense Reasoning (2022.findings-acl)
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| Challenge: | Commonsense reasoning is a language-agnostic process, but most comprehensive knowledge sources are limited to a small number of languages, especially English. |
| Approach: | They propose to use English as a pivot language to integrate commonsense reasoning into models using a translate-retrieve-translate strategy. |
| Outcome: | The proposed model outperforms the state-of-the-art on the XCSR benchmarks. |
Encoding and Fusing Semantic Connection and Linguistic Evidence for Implicit Discourse Relation Recognition (2022.findings-acl)
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| Challenge: | Existing studies use one attention mechanism to improve contextual semantic representation learning for implicit discourse relation recognition (IDRR). |
| Approach: | They propose a Multi-Attentive Neural Fusion model to fuse linguistic evidence and semantic connection for IDRR by using a Dual Attention Network and an Offset Matrix Network. |
| Outcome: | The proposed model achieves state-of-the-art on the PDTB 3.0 corpus. |
One Agent To Rule Them All: Towards Multi-agent Conversational AI (2022.findings-acl)
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Christopher Clarke, Joseph Peper, Karthik Krishnamurthy, Walter Talamonti, Kevin Leach, Walter Lasecki, Yiping Kang, Lingjia Tang, Jason Mars
| Challenge: | Increasing volume of conversational agents (CAs) on the market has resulted in users being burdened with learning and adopting multiple agents to accomplish their tasks. |
| Approach: | They propose a task BBAI: Black-Box Agent Integration that integrates multiple black-box CAs at scale. |
| Outcome: | The proposed system outperforms existing benchmarks in the BBAI: Black-Box Agent Integration task. |
Word-level Perturbation Considering Word Length and Compositional Subwords (2022.findings-acl)
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| Challenge: | Word replacement considering length and compositional word replacement are effective word-level perturbations. |
| Approach: | They propose two simple modifications for word-level perturbation: Word Replacement considering Length and Compositional Word Replacement. |
| Outcome: | The proposed methods improve word-level perturbation and classification performance. |
Bridging Pre-trained Language Models and Hand-crafted Features for Unsupervised POS Tagging (2022.findings-acl)
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| Challenge: | Large-scale pre-trained language models (PLMs) have made extraordinary progress in most NLP tasks, but they fail to achieve state-of-the-art (SOTA) performance. |
| Approach: | They propose a Guassian HMM variant for unsupervised POS tagging that incorporates contexualized word representations into the decoder. |
| Outcome: | The proposed model outperforms state-of-the-art models on Penn Treebank and multilingual Universal Dependencies treebank v2.0. |
Controlling the Focus of Pretrained Language Generation Models (2022.findings-acl)
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| Challenge: | Existing mechanisms to control the model's focus are not available for pretrained transformer-based language generation models. |
| Approach: | They propose to augment a pretrained model with trainable "focus vectors" that are directly applied to the model's embeddings while the model itself is kept fixed. |
| Outcome: | The proposed model is able to generate relevant outputs from user-selected highlights while keeping the model fixed. |
Comparative Opinion Summarization via Collaborative Decoding (2022.findings-acl)
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| Challenge: | Existing opinion summarization methods are insufficient to help users compare multiple choices. |
| Approach: | They propose a comparative opinion summarization task that generates two contrastive summaries and one common summary from two different candidate sets of reviews. |
| Outcome: | The proposed framework produces higher-quality contrastive and common summaries than state-of-the-art models. |
IsoScore: Measuring the Uniformity of Embedding Space Utilization (2022.findings-acl)
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| Challenge: | Several studies suggest that contextualized word embedding models do not isotropically project tokens into vector space. |
| Approach: | They propose to use a tool to measure isotropy to quantify the degree to which a point cloud uniformly utilizes the ambient vector space. |
| Outcome: | The proposed tool is the only available tool that accurately measures how uniformly distributed variance is across dimensions in vector space. |
A Natural Diet: Towards Improving Naturalness of Machine Translation Output (2022.findings-acl)
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| Challenge: | MT evaluation often focuses on accuracy and fluency without paying much attention to translation style. |
| Approach: | They propose a method for training machine translation systems to achieve a more natural style by contrasting training data according to the naturalness of the target side. |
| Outcome: | The proposed method achieves lexical richness on par with human translations, and is preferred by human experts when compared to baseline translations. |
From Stance to Concern: Adaptation of Propositional Analysis to New Tasks and Domains (2022.findings-acl)
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| Challenge: | Existing paradigms for propositional analysis use stances and concerns to generate explanatory representations. |
| Approach: | They propose a generalized paradigm for adaptation of propositional analysis to new tasks and domains by using an analogy between stances and concerns. |
| Outcome: | The proposed model yields 231% improvement in recall over baseline, with only 10% loss in precision. |
CUE Vectors: Modular Training of Language Models Conditioned on Diverse Contextual Signals (2022.findings-acl)
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| Challenge: | Using contextual universal embeddings, we train neural language models on one type of contextual data and adapts to novel context types. |
| Approach: | They propose a framework to modularize the training of neural language models that use diverse forms of context by eliminating the need to jointly train context and within-sentence encoders. |
| Outcome: | The proposed framework trains LMs on one type of contextual data and adapts to novel context types. |
Cross-Lingual UMLS Named Entity Linking using UMLS Dictionary Fine-Tuning (2022.findings-acl)
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| Challenge: | a new method for named entity linking is being developed in the field of public health . it uses an offline unsupervised construction of a translated dictionary and a pre-trained transformer language model to filter candidates according to context. |
| Approach: | They propose a method for mapping mentions in a source language to UMLS concepts . they extend an offline unsupervised translation of a translated UMLS dictionary . |
| Outcome: | The proposed approach achieves state-of-the-art on the Hebrew Camoni corpus and English datasets. |
Aligned Weight Regularizers for Pruning Pretrained Neural Networks (2022.findings-acl)
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| Challenge: | Pruning aims to reduce the number of parameters while maintaining performance close to the original network. |
| Approach: | They propose a self-distilled pruning strategy that maximizes representational similarity between pruned and unpruned networks. |
| Outcome: | The proposed pruning strategy outperforms smaller models and outperformed smaller ones with an equal number of parameters and is competitive against (6 times) larger distilled networks. |
Consistent Representation Learning for Continual Relation Extraction (2022.findings-acl)
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| Challenge: | Existing methods to train relation extraction models overfit memory samples and perform poorly on imbalanced datasets. |
| Approach: | They propose a method which uses contrastive learning and knowledge distillation to train a model on data with new relations while avoiding forgetting old ones. |
| Outcome: | The proposed method significantly outperforms state-of-the-art baselines and yields strong robustness on the imbalanced datasets. |
Event Transition Planning for Open-ended Text Generation (2022.findings-acl)
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| Challenge: | Open-ended text generation tasks require models to generate coherent continuation given limited preceding context. |
| Approach: | They propose a novel two-stage method which explicitly arranges ensuing events in open-ended text generation tasks. |
| Outcome: | The proposed method improves coherence and diversity of open-ended text generation tasks. |
Comprehensive Multi-Modal Interactions for Referring Image Segmentation (2022.findings-acl)
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| Challenge: | Existing methods for RIS compute different forms of interactions sequentially or ignore intra-modal interactions. |
| Approach: | They propose a method which outputs a segmentation map corresponding to the natural language description. |
| Outcome: | The proposed method performs on four benchmark datasets and shows significant performance gains over the existing state-of-the-art methods. |
MetaWeighting: Learning to Weight Tasks in Multi-Task Learning (2022.findings-acl)
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| Challenge: | Existing task weighting methods assign weights only based on training losses, while ignoring the gap between the training loss and generalization loss. |
| Approach: | They propose a task weighting algorithm which automatically weights the tasks via a learning-to-learn paradigm and a multi-task text classification paradigm. |
| Outcome: | Extensive experiments show that the proposed method outperforms existing methods in multi-task text classification. |
Improving Controllable Text Generation with Position-Aware Weighted Decoding (2022.findings-acl)
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| Challenge: | Controllable text generation is a challenging task in natural language generation, which aims to generate diverse text related to specified attributes. |
| Approach: | They propose a framework that uses a lightweight controller to adjust bias signals from the controller at different decoding positions. |
| Outcome: | Experiments on positive sentiment control, topic control, and language detoxification show the proposed framework works on 4 SOTA models. |
Prompt Tuning for Discriminative Pre-trained Language Models (2022.findings-acl)
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Yuan Yao, Bowen Dong, Ao Zhang, Zhengyan Zhang, Ruobing Xie, Zhiyuan Liu, Leyu Lin, Maosong Sun, Jianyong Wang
| Challenge: | Recent studies have shown promising results of prompt tuning in stimulating pre-trained language models (PLMs) for natural language processing tasks. |
| Approach: | They propose a prompt tuning framework that reformulates NLP tasks into a discriminative language modeling problem. |
| Outcome: | The proposed framework improves on text classification and question answering tasks and prevents unstable tuning problems in low-resource settings. |
Two Birds with One Stone: Unified Model Learning for Both Recall and Ranking in News Recommendation (2022.findings-acl)
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| Challenge: | Existing news recommender systems conduct news recall and ranking separately with different models, but maintaining multiple models leads to high computational cost and high latency. |
| Approach: | They propose a unified method for recall and ranking in news recommendation that uses historical news click behaviors to extract user embeddings for ranking from the user's attention query. |
| Outcome: | The proposed method improves recall and ranking efficiency and effectiveness on a benchmark dataset. |
What does it take to bake a cake? The RecipeRef corpus and anaphora resolution in procedural text (2022.findings-acl)
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| Challenge: | Current research on anaphora resolution is mostly based on declarative text, such as chemical patents or instruction manuals. |
| Approach: | They propose a framework for anaphora annotation for the chemical domain for modeling anamorphic phenomena in recipes and chemical patents. |
| Outcome: | The proposed framework improves resolution of anaphora in recipes, suggesting transferability of general procedural knowledge. |
MERIt: Meta-Path Guided Contrastive Learning for Logical Reasoning (2022.findings-acl)
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| Challenge: | Existing methods to infer logical relations with annotated training data suffer from over-fitting and poor generalization problems due to the dataset sparsity. |
| Approach: | They propose a MEta-path guided contrastive learning method for logical ReasonIng of text that performs self-supervised pre-training on abundant unlabeled text data. |
| Outcome: | The proposed method outperforms the baselines on two logical reasoning benchmarks with significant improvements. |
THE-X: Privacy-Preserving Transformer Inference with Homomorphic Encryption (2022.findings-acl)
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Tianyu Chen, Hangbo Bao, Shaohan Huang, Li Dong, Binxing Jiao, Daxin Jiang, Haoyi Zhou, Jianxin Li, Furu Wei
| Challenge: | enabling pre-trained models inference on ciphertext data is difficult due to the complex computations in transformer blocks. |
| Approach: | They propose an approximation approach for transformers which enables inference on ciphertext data. |
| Outcome: | The proposed approach can infer pre-trained models on encrypted data with negligible performance drop but enjoy theory-guaranteed privacy-preserving advantage. |
HLDC: Hindi Legal Documents Corpus (2022.findings-acl)
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Arnav Kapoor, Mudit Dhawan, Anmol Goel, Arjun T H, Akshala Bhatnagar, Vibhu Agrawal, Amul Agrawal, Arnab Bhattacharya, Ponnurangam Kumaraguru, Ashutosh Modi
| Challenge: | Existing systems that process legal documents are lacking high-quality corpora in low resource languages such as Hindi. |
| Approach: | They propose a Hindi Legal Documents Corpus (HLDC) that contains 900K legal documents in Hindi. |
| Outcome: | The proposed model is based on a corpus of more than 900K legal documents in Hindi. |
Rethinking Document-level Neural Machine Translation (2022.findings-acl)
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| Challenge: | Neural machine translation models are weak enough for document-level translation . current models only translate sentences individually, resulting in poor document coherence . |
| Approach: | They propose to use the original Transformer model to test document-level neural machine translation . they find that the original transformer models can achieve strong results for document translation if trained properly . |
| Outcome: | The proposed model outperforms sentence-level models on nine datasets and two sentence- level datasets across six languages. |
Incremental Intent Detection for Medical Domain with Contrast Replay Networks (2022.findings-acl)
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| Challenge: | Existing methods to detect medical intents require fixed pre-defined intent categories . however, novel medical intent categories incessantly emerge with new data and intents in the real world . |
| Approach: | They propose to incrementally learn emerged medical intents from continually arriving data of new intents while avoiding catastrophically forgetting old ones. |
| Outcome: | The proposed method outperforms the state-of-the-art model on two benchmarks by 5.7% and 9.1% accuracy. |
LaPraDoR: Unsupervised Pretrained Dense Retriever for Zero-Shot Text Retrieval (2022.findings-acl)
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| Challenge: | Experimental results show that LaPraDoR is state-of-the-art compared with supervised dense retrieval models. |
| Approach: | They propose a pretrained dual-tower dense retriever that does not require supervised data for training. |
| Outcome: | The proposed method achieves state-of-the-art performance on 18 datasets of 9 zero-shot text retrieval tasks. |
Do Pre-trained Models Benefit Knowledge Graph Completion? A Reliable Evaluation and a Reasonable Approach (2022.findings-acl)
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| Challenge: | Pre-trained language models capture factual knowledge from massive texts . but they are still quite behind the SOTA KGC models in terms of performance . |
| Approach: | They propose to use open-world assumption to evaluate PLM-based knowledge graph completion models . they propose to convert each triple and its support information into natural prompt sentences . |
| Outcome: | The proposed model is more accurate under the open-world assumption (OWA) this setting manual checks the correctness of knowledge that is not in KGs. |
EICO: Improving Few-Shot Text Classification via Explicit and Implicit Consistency Regularization (2022.findings-acl)
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| Challenge: | Existing methods for few-shot text classification are limited by labeled data. |
| Approach: | They propose to use consistency regularization to improve few-shot text classification by generating pseudo-labels from weakly-augmented and strongly-augmented views. |
| Outcome: | The proposed method achieves competitive performance with 16 labeled examples with prompt and verbalizer. |
Improving the Adversarial Robustness of NLP Models by Information Bottleneck (2022.findings-acl)
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| Challenge: | Existing studies have shown that adversarial examples can be directly attributed to the presence of non-robust features. |
| Approach: | They propose to capture task-specific robust features while eliminating non-robust ones . they show that models can achieve significant improvement in robust accuracy . |
| Outcome: | The proposed method outperforms all defense methods on SST-2, AGNEWS and IMDB datasets while achieving no performance drop. |
Incorporating Dynamic Semantics into Pre-Trained Language Model for Aspect-based Sentiment Analysis (2022.findings-acl)
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| Challenge: | Aspect-based sentiment analysis (ABSA) predicts sentiment polarity towards a specific aspect in a sentence. |
| Approach: | They propose to use a dynamic aspect-oriented semantics-based method to learn ABSA. |
| Outcome: | The proposed method can learn dynamic aspect-oriented semantics for ABSA on three benchmark datasets. |
DARER: Dual-task Temporal Relational Recurrent Reasoning Network for Joint Dialog Sentiment Classification and Act Recognition (2022.findings-acl)
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| Challenge: | Dialog sentiment classification (DSC) and dialog act recognition (DAR) aims to predict the sentiment label and act label for each utterance in a dialog. |
| Approach: | They propose a framework which integrates prediction-level interactions other than semantics-level ones into dialog understanding and dual-task reasoning by integrating temporal relations into the model. |
| Outcome: | The proposed model outperforms existing models by large margins while costing less training time and requiring less computation resource. |
Divide and Conquer: Text Semantic Matching with Disentangled Keywords and Intents (2022.findings-acl)
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| Challenge: | Existing text semantic matching models do not provide granularity for text comparison. |
| Approach: | They propose a simple yet effective training strategy for text semantic matching by disentangling keywords from intents. |
| Outcome: | The proposed approach achieves stable performance improvements against a wide range of models on three benchmarks. |
Modular Domain Adaptation (2022.findings-acl)
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| Challenge: | Existing models for sentiment analysis and hate speech detection are difficult to account for domain shift without access to source data. |
| Approach: | They propose to treat domain adaptation as a modular process that involves separate model producers and model consumers . they demonstrate that they can independently cooperate to facilitate more accurate measurements of text . |
| Outcome: | The proposed methods improve out-of-domain accuracy on four multi-domain text classification datasets. |
Detection of Adversarial Examples in Text Classification: Benchmark and Baseline via Robust Density Estimation (2022.findings-acl)
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| Challenge: | Word-level adversarial attacks have shown success in NLP, decreasing performance of transformer-based models with smaller perturbation rate. |
| Approach: | They propose a dataset for four popular attack methods on four datasets and four models to encourage further research in this field. |
| Outcome: | The proposed baseline has the highest auc on 29 out of 30 dataset-attack-model combinations. |
Platt-Bin: Efficient Posterior Calibrated Training for NLP Classifiers (2022.findings-acl)
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| Challenge: | Existing methods for posterior calibration return uncalibrated estimations of class posteriors, thus leading to poorer generalization. |
| Approach: | They propose an end-to-end trained calibrator that directly optimizes the objective while minimizing the difference between predicted and empirical posterior probabilities. |
| Outcome: | The proposed calibrator reduces calibration error and improves performance on benchmark NLP classification tasks. |
Addressing Resource and Privacy Constraints in Semantic Parsing Through Data Augmentation (2022.findings-acl)
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| Challenge: | a low-resource task-oriented semantic parser is limited by privacy requirements for unlabeled natural utterances. |
| Approach: | They propose a setup for low-resource task-oriented semantic parsing based on user interactions . they use structured canonical utterances, then simulating corresponding natural language to improve performance. |
| Outcome: | The proposed setup improves on a low-resource task-oriented semantic parser using utterances collected through user interactions. |
Improving Candidate Retrieval with Entity Profile Generation for Wikidata Entity Linking (2022.findings-acl)
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| Challenge: | Existing studies focus on Wikipedia-derived KBs, but there is little work on EL over Wikidata . EL systems have found applications in many tasks such as question answering . |
| Approach: | They propose a novel approach to linking entity mentions to referent entities in a knowledge base . they use a sequence-to-sequence model to generate the profile of the target entity . |
| Outcome: | The proposed approach achieves state-of-the-art results on three Wikidata-based datasets and strong performance on TACKBP-2010. |
Local Structure Matters Most: Perturbation Study in NLU (2022.findings-acl)
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| Challenge: | Recent research shows that neural models are insensitive to word-order perturbations, but other studies suggest that models learn some abstract notion of syntax. |
| Approach: | They develop order-altering perturbations on the order of words, subwords, and characters to analyze their effect on neural models’ performance on language understanding tasks. |
| Outcome: | The proposed models are insensitive to word-order perturbations while the local ordering remains relatively unperturbed. |
Probing Factually Grounded Content Transfer with Factual Ablation (2022.findings-acl)
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| Challenge: | Despite recent success, large neural models often generate factually incorrect text . lack of a standard evaluation for factuality complicates factual grounded generation . |
| Approach: | They propose a method to measure factual consistency by presenting two evaluation sets . large pretrained models have shown impressive effectiveness at longstanding tasks . |
| Outcome: | The proposed method improves over strong baselines by presenting two evaluation sets. |
ED2LM: Encoder-Decoder to Language Model for Faster Document Re-ranking Inference (2022.findings-acl)
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Kai Hui, Honglei Zhuang, Tao Chen, Zhen Qin, Jing Lu, Dara Bahri, Ji Ma, Jai Gupta, Cicero Nogueira dos Santos, Yi Tay, Donald Metzler
| Challenge: | State-of-the-art neural models typically encode document-query pairs using cross-attention for re-ranking. |
| Approach: | They propose to fine tune a pretrained encoder-decoder model using document to query generation. |
| Outcome: | The proposed model achieves comparable results to more expensive approaches while being 6.8X faster. |
Benchmarking Answer Verification Methods for Question Answering-Based Summarization Evaluation Metrics (2022.findings-acl)
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| Challenge: | Existing QA-based summarization metrics must automatically determine whether the QA model’s prediction is correct or not. |
| Approach: | They benchmark lexical answer verification methods used by current QA-based metrics and two more sophisticated text comparison methods, BERTScore and LERC. |
| Outcome: | The proposed methods outperform the other methods in some settings while remaining statistically indistinguishable from lexical overlap in others. |
Prior Knowledge and Memory Enriched Transformer for Sign Language Translation (2022.findings-acl)
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| Challenge: | Existing methods for sign language translation do not explore all of them . visual and textual understanding and additional prior knowledge learning are challenging . |
| Approach: | They propose a method which integrates auxiliary information into vanilla transformer for SLT . they use visual-textual context information and additional auxiliary knowledge of a word . |
| Outcome: | The proposed method improves the understanding of sign language videos with visual and textual understanding and additional prior knowledge learning. |
Discontinuous Constituency and BERT: A Case Study of Dutch (2022.findings-acl)
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| Challenge: | a recent study has shown that large-scale language models fail to acquire aspects of linguistic theory due to their unanticipated performance. |
| Approach: | They propose to use a context-sensitive formalism to derive grammars that capture verb nesting and verb raising in Dutch. |
| Outcome: | The proposed model fails to acquire the dependencies examined in Dutch. |
Probing Multilingual Cognate Prediction Models (2022.findings-acl)
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| Challenge: | linguistic interpretations of cognate prediction have been based on external analysis (accuracy, raw results, errors). |
| Approach: | They propose to use character-based machine translation models to store linguistic and diachronic information but not in previously assumed ways. |
| Outcome: | The proposed model stores linguistic and diachronic information but does not achieve it in previously assumed ways. |
A Neural Pairwise Ranking Model for Readability Assessment (2022.findings-acl)
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| Challenge: | Automatic Readability Assessment (ARA) is traditionally treated as a classification problem in NLP research. |
| Approach: | They propose a neural ranking approach to automatic readability assessment (ARA) they propose 'neural' ranking methods that can be used to rank texts by reading level . |
| Outcome: | The proposed approach performs well in monolingual single/cross corpus testing scenarios and achieves a zero-shot cross-lingual ranking accuracy of over 80% for both French and Spanish when trained on English data. |
First the Worst: Finding Better Gender Translations During Beam Search (2022.findings-acl)
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| Challenge: | Neural language generation models optimized by likelihood tend towards 'safe' word choice. |
| Approach: | They propose to use beam search to improve gender diversity in n-best lists and rerank n best lists using gender features obtained from the source sentence to address this problem. |
| Outcome: | The proposed approach improves gender diversity in n-best lists and reranks n best lists using gender features obtained from the source sentence. |
Dialogue Summaries as Dialogue States (DS2), Template-Guided Summarization for Few-shot Dialogue State Tracking (2022.findings-acl)
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| Challenge: | Annotating task-oriented dialogues is notorious for the expensive and difficult data collection process. |
| Approach: | They propose to reformulate dialogue state tracking as a dialogue summarization problem by using synthetic dialogue summaries generated by a set of rules. |
| Outcome: | The proposed method outperforms previous studies on few-shot dialogue state tracking in MultiWoZ 2.0 and 2.1 in cross-domain and multi-domain settings. |
Unsupervised Preference-Aware Language Identification (2022.findings-acl)
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| Challenge: | Existing studies do not consider inter-personal variations due to the lack of user annotated training data. |
| Approach: | They propose to use user preferences to identify ambiguous texts in multilingual applications without user annotated training data to build a preference-aware LID model. |
| Outcome: | The proposed model significantly outperforms existing LID systems on handling ambiguous texts. |
Using NLP to quantify the environmental cost and diversity benefits of in-person NLP conferences (2022.findings-acl)
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| Challenge: | Figure 1 shows the increase in travel to the ACL annual meeting over the past 40 years . |
| Approach: | They analyse the carbon cost associated with journeys made by researchers attending in-person NLP conferences by text-mining publications from the ACL anthology . |
| Outcome: | The proposed model compares the carbon cost associated with travel to in-person conferences to previously known values for training large models. |
Interpretable Research Replication Prediction via Variational Contextual Consistency Sentence Masking (2022.findings-acl)
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| Challenge: | Existing methods for predicting research replication are insufficient especially for long research papers. |
| Approach: | They propose to build an interpretable neural model which can provide sentence-level explanations and apply weakly supervised approach to leverage large corpus of unlabeled datasets. |
| Outcome: | The proposed model can provide sentence-level explanations and leverage large unlabeled datasets to boost interpretability and improve prediction performance. |
Chinese Synesthesia Detection: New Dataset and Models (2022.findings-acl)
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| Challenge: | Synesthesia refers to the description of perceptions in one sensory modality through concepts from other modalities. |
| Approach: | They propose a task called synesthesia detection to extract the sensory word of a sentence and predict the original and synesthetic sensory modalities of the corresponding sensory word. |
| Outcome: | The proposed model achieves state-of-the-art on the Chinese synesthesia dataset. |
Rethinking Offensive Text Detection as a Multi-Hop Reasoning Problem (2022.findings-acl)
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| Challenge: | Existing methods of offensive text detection perform poorly when asked to detect implicitly offensive statements . a dataset based on SLIGHT provides a framework for implicit offensive text identification . |
| Approach: | They propose a dataset to support the task of implicit offensive text detection in dialogues . they show that reasoning is crucial for understanding this broader class of offensive utterances - SLIGHT . |
| Outcome: | The proposed model achieves 11% accuracy in implicit offensive text detection tasks . the proposed model can be used to identify toxic speech in specific domains . |
On the Safety of Conversational Models: Taxonomy, Dataset, and Benchmark (2022.findings-acl)
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Hao Sun, Guangxuan Xu, Jiawen Deng, Jiale Cheng, Chujie Zheng, Hao Zhou, Nanyun Peng, Xiaoyan Zhu, Minlie Huang
| Challenge: | Dialogue safety problems severely limit the real-world deployment of generative conversational models. |
| Approach: | They propose a taxonomy for dialogue safety specifically designed to capture unsafe behaviors in human-bot dialogue settings. |
| Outcome: | The proposed taxonomy captures unsafe behaviors in human-bot dialogue settings with rich context-sensitive unsafe examples. |
Word Segmentation by Separation Inference for East Asian Languages (2022.findings-acl)
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| Challenge: | Chinese Word Segmentation (CWS) is a sequence labeling task that divides sentences into words . despite diverse tagging schemas, they all carry implicit position information. |
| Approach: | They propose to model the separation state of every two consecutive characters by tagging them as two tags. |
| Outcome: | The proposed framework outperforms state-of-the-art on Japanese and Korean Word Segmentation datasets. |
Unsupervised Chinese Word Segmentation with BERT Oriented Probing and Transformation (2022.findings-acl)
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| Challenge: | Existing methods for unsupervised Chinese word segmentation exploit shallow semantic information, which can miss important context. |
| Approach: | They propose to take advantage of deep contextual semantic information with a self-training manner to transform it into explicit word segmentation ability. |
| Outcome: | The proposed approach achieves state-of-the-art F1 score on two CWS benchmark datasets. |
E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning (2022.findings-acl)
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Jiangjie Chen, Rui Xu, Ziquan Fu, Wei Shi, Zhongqiao Li, Xinbo Zhang, Changzhi Sun, Lei Li, Yanghua Xiao, Hao Zhou
| Challenge: | Existing benchmarks to test word analogy do not reveal the underneath process of analogical reasoning of neural models. |
| Approach: | They propose an explanation benchmark for analogical reasoning using a Civil Service exam . they use a free-text explanation scheme to explain whether an analogy should be drawn . |
| Outcome: | The proposed benchmark is very challenging for state-of-the-art models, it is found. |
Implicit Relation Linking for Question Answering over Knowledge Graph (2022.findings-acl)
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| Challenge: | Existing methods rely on textual similarities between NL and KG to build relation links. |
| Approach: | They propose an implicit relation linking method called ImRL which links relation phrases in NL to relation paths in KG. |
| Outcome: | The proposed method significantly outperforms state-of-the-art methods on two benchmarks and a newly-created datasets. |
Attention Mechanism with Energy-Friendly Operations (2022.findings-acl)
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| Challenge: | Empirical results show that attention mechanism can be improved from the energy consumption aspects. |
| Approach: | They propose to replace multiplications with either selective operations or additions to reduce energy consumption. |
| Outcome: | The proposed model achieves competitable accuracy while saving 99% and 66% energy during alignment calculation and the whole attention procedure. |
Probing BERT’s priors with serial reproduction chains (2022.findings-acl)
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| Challenge: | Large neural language models have induced surprisingly human-like linguistic knowledge, from syntactic structure and subtle lexical biases to more insidious social biase and stereotypes. |
| Approach: | They propose to use serial reproduction chains to generate representative samples from popular masked language models like BERT to test their hypothesis. |
| Outcome: | The proposed method is based on theories of iterated learning in cognitive science and can be used to probe masked language models. |
Interpreting the Robustness of Neural NLP Models to Textual Perturbations (2022.findings-acl)
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| Challenge: | Modern Natural Language Processing models are sensitive to input perturbations and their performance can decrease when applied to noisy data. |
| Approach: | They propose to explain the extent to which a model is affected by an unseen textual perturbation by the learnability of the perturbation. |
| Outcome: | The proposed model is better at identifying a perturbation (higher learnability) but worse at ignoring it (lower robustness). |
Zero-Shot Dense Retrieval with Momentum Adversarial Domain Invariant Representations (2022.findings-acl)
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| Challenge: | Dense retrieval (DR) methods first encode texts into a dense embedding space and then conduct text retrieval using efficient nearest neighbor search. |
| Approach: | They propose Momentum adversarial Domain Invariant Representation learning to train a domain classifier that distinguishes source versus target domains and adversarially updates the DR encoder to learn domain invariant representations. |
| Outcome: | The proposed method outperforms baselines on 10+ ranking datasets collected in the BEIR benchmark in the zero-shot setting, with more than 10% relative gains on datasets with enough sensitivity for DR models’ evaluation. |
A Few-Shot Semantic Parser for Wizard-of-Oz Dialogues with the Precise ThingTalk Representation (2022.findings-acl)
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| Challenge: | Existing approaches to build effective semantic parsers for Wizard-of-Oz are insufficient. |
| Approach: | They propose a new dialogue representation and a sample-efficient methodology that can predict precise dialogue states in WOZ conversations. |
| Outcome: | The proposed model can predict precise dialogue states in WOZ conversations. |
GCPG: A General Framework for Controllable Paraphrase Generation (2022.findings-acl)
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Kexin Yang, Dayiheng Liu, Wenqiang Lei, Baosong Yang, Haibo Zhang, Xue Zhao, Wenqing Yao, Boxing Chen
| Challenge: | Existing studies highlight a special condition under two indispensable aspects of controllable paraphrase generation (CPG) individually, lacking a unified circumstance to explore and analyze their effectiveness. |
| Approach: | They propose a general controllable paraphrase generation framework that integrates lexical and syntactical conditions into a text sequence and uniformly processes them in an encoder-decoder paradigm. |
| Outcome: | The proposed framework can combine lexical and syntactical conditions and improve paraphrase generation. |
CrossAligner & Co: Zero-Shot Transfer Methods for Task-Oriented Cross-lingual Natural Language Understanding (2022.findings-acl)
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| Challenge: | Task-oriented personal assistants enable people to interact with devices and services using natural language. |
| Approach: | They propose a method to acquire task knowledge in a high-resource language and then transfer it to the low-resourced language(s) they use unlabelled parallel data to perform a quantitative analysis of the methods. |
| Outcome: | The proposed methods exceed state-of-the-art (SOTA) scores across nine languages, fifteen test sets and three benchmark multilingual datasets. |
Attention as Grounding: Exploring Textual and Cross-Modal Attention on Entities and Relations in Language-and-Vision Transformer (2022.findings-acl)
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| Challenge: | Existing work has focused on what is captured by multi-modal architectures. |
| Approach: | They propose a multi-modal transformer that learns syntactic and semantic representations about entities and relations grounded in objects at the level of masked self-attention and cross-modal attention. |
| Outcome: | The proposed model learns syntactic and semantic representations about objects and relations cross-modally and unimodally. |
Improving Zero-Shot Cross-lingual Transfer Between Closely Related Languages by Injecting Character-Level Noise (2022.findings-acl)
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| Challenge: | Existing approaches to improve cross-lingual transfer do not take surface similarity into account. |
| Approach: | They propose to augment source language training data with character-level noise to simulate spelling variations. |
| Outcome: | The proposed strategy shows consistent improvements over several languages and tasks. |
Structural Supervision for Word Alignment and Machine Translation (2022.findings-acl)
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| Challenge: | Existing knowledge on syntactic structure neglects the rich structural information from target tokens and the structural similarity between the source and target sentences. |
| Approach: | They propose to incorporate syntactic structure of both source and target tokens into the encoder-decoder framework, tightly correlating the internal logic of word alignment and machine translation for multi-task learning. |
| Outcome: | The proposed method outperforms baselines on four publicly available language pairs and consistently outperformed baselines in alignment accuracy and translation quality. |
Focus on the Action: Learning to Highlight and Summarize Jointly for Email To-Do Items Summarization (2022.findings-acl)
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| Challenge: | Existing to-do item generation models focus on generating action mentions to provide more structured summaries of email text. |
| Approach: | They propose a learning to highlight and summarize framework to learn to identify the most salient text and actions and incorporate these structured representations to generate more faithful to-do items. |
| Outcome: | The proposed model outperforms baseline models and achieves state-of-the-art performance in terms of evaluation and human judgement. |
Exploring the Capacity of a Large-scale Masked Language Model to Recognize Grammatical Errors (2022.findings-acl)
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| Challenge: | a language model-based error detection method can learn errors with a small training sample. |
| Approach: | They propose a language model-based method for grammatical error detection with feedback comments. |
| Outcome: | The proposed method can learn errors with a little training data and improve recall faster than non-language models. |
Should We Trust This Summary? Bayesian Abstractive Summarization to The Rescue (2022.findings-acl)
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| Challenge: | Xu et al., 2019; Lewis e t al, 2019) show that Bayesian summarization methods can generate high quality summaries but suffer from a couple of issues when inputs lie far from the training data distribution. |
| Approach: | They propose to extend state-of-the-art summarization models with Monte Carlo dropout and perform multiple stochastic forward passes to approximate Bayesian inference. |
| Outcome: | The proposed method outperforms deterministic summarization models on multiple benchmark datasets. |
On the data requirements of probing (2022.findings-acl)
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| Challenge: | Existing methods to probe neural networks are expensive and require large datasets. |
| Approach: | They propose a method to estimate the required number of data samples in probing datasets . they use a classification task to encode a text with a deep neural network . |
| Outcome: | The proposed method estimates the required number of data samples in two probing configurations and proves it is statistically powerful. |
Translation Error Detection as Rationale Extraction (2022.findings-acl)
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| Challenge: | Recent Quality Estimation models rely on translation errors to predict overall sentence quality, but detecting specific errors is a more challenging task. |
| Approach: | They propose to use a semi-supervised method to detect translation errors by attribution of relevance scores to inputs to explain model predictions. |
| Outcome: | The proposed method can detect translation errors and is compared with human models using a set of feature attribution methods. |
Towards Collaborative Neural-Symbolic Graph Semantic Parsing via Uncertainty (2022.findings-acl)
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| Challenge: | Recent work in task-independent graph semantic parsing has shifted from symbolic approaches to neural models, showing strong performance on different types of meaning representations. |
| Approach: | They propose a framework that incorporates prior knowledge from a symbolic parser into a decision criterion for beam search to address these limitations. |
| Outcome: | The proposed framework improves on the in-distribution test set but degrades significantly on long-tail situations while the symbolic parser performs more robustly. |
Towards Few-shot Entity Recognition in Document Images: A Label-aware Sequence-to-Sequence Framework (2022.findings-acl)
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| Challenge: | Entity recognition is a fundamental task in document image understandings. |
| Approach: | They propose to use label surface names to better inform a model of target entity type semantics and embed the labels into the spatial embedding space to capture spatial correspondence between regions and labels. |
| Outcome: | The proposed model can be built on a few shots of annotated document images . it can be used to better inform the model and capture spatial correspondence between regions . |
On Length Divergence Bias in Textual Matching Models (2022.findings-acl)
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| Challenge: | Existing deep models have been successful in textual matching tasks, but it is unclear whether they understand language or measure semantic similarity of texts. |
| Approach: | They propose an adversarial evaluation scheme which invalidates the length divergence bias in TM datasets. |
| Outcome: | The proposed method improves the robustness and generalization ability of models at the same time. |
What is wrong with you?: Leveraging User Sentiment for Automatic Dialog Evaluation (2022.findings-acl)
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| Challenge: | Existing metrics for dialog evaluation are trained on human annotations, which is cumbersome to collect. |
| Approach: | They propose to use user sentiment and other information as proxy to measure the quality of previous dialogs. |
| Outcome: | The proposed model is comparable to models trained on human annotated data. |