Papers with supervised setting
SKICSE: Sentence Knowable Information Prompted by LLMs Improves Contrastive Sentence Embeddings (2024.naacl-short)
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| Challenge: | Experimental results show that our model outperforms the previous state-of-the-art model GloVe on STS tasks. |
| Approach: | They propose a simple and effective prompt template that is able to obtain the knowable information of input sentences from LLMs. |
| Outcome: | The proposed model outperforms the previous state-of-the-art model PromptBERT on STS tasks. |
Coreference Resolution through a seq2seq Transition-Based System (2023.tacl-1)
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| Challenge: | Recent coreference resolution systems use search algorithms to identify mentions and resolve coreference. |
| Approach: | They propose a text-to-text coreference resolution system that uses a semantic paradigm to predict mentions and links jointly. |
| Outcome: | The proposed system achieves state-of-the-art accuracy on CoNLL-2012 datasets with 83.3 F1-score for English, 68.5 F1 score for Arabic, and 74.3 F1 scores for Chinese. |
Can a Gorilla Ride a Camel? Learning Semantic Plausibility from Text (D19-60)
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| Challenge: | Existing work on modeling semantic plausibility has focused on physical plausability but distributional methods fail when tested in supervised settings. |
| Approach: | They propose to use large pretrained language models to model plausibility in supervised settings by extracting attested events from a large corpus and injecting explicit commonsense knowledge into a distributional model. |
| Outcome: | The proposed model is effective in modeling plausibility in a supervised setting. |
AMR-RE: Abstract Meaning Representations for Retrieval-Based In-Context Learning in Relation Extraction (2025.naacl-srw)
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| Challenge: | Existing in-context learning methods for relation extraction often overlook entity relationships . Existing methods for RE prioritize language similarity over structural similarity . |
| Approach: | They propose an AMR-enhanced retrieval-based ICL method for relation extraction . their method retrieves in-context examples based on semantic structure similarity . |
| Outcome: | The proposed method outperforms baselines on four English RE datasets and in the more demanding unsupervised setting. |
Label Anchored Contrastive Learning for Language Understanding (2022.naacl-main)
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| Challenge: | a novel approach to contrastive learning for language understanding is not fully explored . contrastive training has been widely applied to self-supervised representation learning . |
| Approach: | They propose a label anchored contrastive learning approach for language understanding using a class label. |
| Outcome: | The proposed approach improves on GLUE and CLUE benchmarks by 4.1% compared to the state-of-the-art approaches . the proposed approach also improves under the few-shot and data imbalance settings . |
Connecting the Dots: Inferring Patent Phrase Similarity with Retrieved Phrase Graphs (2024.findings-naacl)
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| Challenge: | Existing methods for inferring patent phrase similarity do not perform satisfactorily . et al., 2010: patents are pivotal to innovation, safeguarding novel ideas . |
| Approach: | They propose a graph-augmented approach to amplify patent phrase contextual information . they construct a phrase graph that links to patents cited by or cited in patents for each phrase . |
| Outcome: | The proposed approach significantly improves the representation of patent phrases in a self-supervised fashion. |
Unsupervised Information Extraction: Regularizing Discriminative Approaches with Relation Distribution Losses (P19-1)
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| Challenge: | Existing unsupervised relation extraction models are either generative or discriminative . however, they are hard to train without supervision and are unstable . |
| Approach: | They propose a skewness loss and distribution distance loss to improve the performance of discriminative based models. |
| Outcome: | The proposed models surpass current state-of-the-art on three different datasets. |
Sentence and Clause Level Emotion Annotation, Detection, and Classification in a Multi-Genre Corpus (L18-1)
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| Challenge: | Existing methods for predicting emotion categories are limited due to their multi-label nature . e.g. anger, joy, sadness are difficult to predict due to inherent multi-genre nature - a problem that is often overlooked in single-genrete text. |
| Approach: | They propose to expand existing annotated data to include 8 emotions from Plutchik's Wheel of Emotions . they explore the effectiveness of clause annotation in sentence-level emotion detection and classification . |
| Outcome: | The proposed system is the first to target the clause level and provides emotion classification for movie reviews datasets. |
LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Prompting (2022.coling-1)
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Xiang Chen, Lei Li, Shumin Deng, Chuanqi Tan, Changliang Xu, Fei Huang, Luo Si, Huajun Chen, Ningyu Zhang
| Challenge: | Existing approaches for Named Entity Recognition (NER) use extensive labeled data for model training, which struggles in low-resource scenarios. |
| Approach: | They propose a lightweight tuning paradigm for low-resource NER via pluggable prompting . they construct a learnable verbalizer of entity categories without any label-specific classifiers . |
| Outcome: | The proposed model outperforms baselines and class transfer models in low-resource scenarios. |
Conversation Disentanglement with Bi-Level Contrastive Learning (2022.findings-emnlp)
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| Challenge: | Existing methods focus on pairwise utterance relations but pay inadequate attention to utterant-to-context relation modeling. |
| Approach: | They propose a general disentangle model based on bi-level contrastive learning that brings closer utterances in the same session while encouraging each utterrance to be near its clustered session prototypes in representation space. |
| Outcome: | The proposed model achieves state-of-the-art performance on both settings across public datasets. |
Cross-Domain NER using Cross-Domain Language Modeling (P19-1)
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| Challenge: | Existing methods for named entity recognition (NER) use labeled data for both source and target domains. |
| Approach: | They propose to use language modeling as a bridge between NER domains to perform cross-domain and cross-task knowledge transfer. |
| Outcome: | The proposed method extracts domain differences from cross-domain LM contrast, allowing unsupervised domain adaptation while giving state-of-the-art results among supervised domain adapters. |
SANTA: Separate Strategies for Inaccurate and Incomplete Annotation Noise in Distantly-Supervised Named Entity Recognition (2023.findings-acl)
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| Challenge: | Distantly-Supervised Named Entity Recognition (DS-NER) is widely used in the supervised setting, but the context-free matching process and the limited coverage of knowledge bases introduce inaccurate and incomplete annotation noise respectively. |
| Approach: | They propose to handle two types of noise separately with Memory-smoothed Focal Loss and Entity-aware KNN to relieve the entity ambiguity problem caused by inaccurate annotation and a noise-tolerant loss to improve the model’s robustness. |
| Outcome: | The proposed model achieves a new state-of-the-art on five public datasets. |
Morphologically Aware Word-Level Translation (2020.coling-main)
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| Challenge: | Current approaches to bilingual lexicon induction (BLI) ignore inflectional morphology . current models degrade when translating less frequent inflected forms . |
| Approach: | They propose a morphologically aware probability model that models lexeme translation and inflectional morphology in a structured way. |
| Outcome: | The proposed model improves performance across 6 language pairs and in the weakly supervised setting. |
Combining Static Word Embeddings and Contextual Representations for Bilingual Lexicon Induction (2021.findings-acl)
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| Challenge: | Bilingual Lexicon Induction (BLI) aims to map words in one language to their translations in another. |
| Approach: | They propose a mechanism to combine static word embeddings and contextual representations to utilize the advantages of both paradigms. |
| Outcome: | The proposed method improves performance on supervised and unsupervised BLI benchmarks on all language pairs by average improving 3.2 points over baselines. |
NIPS Conversational Intelligence Challenge 2017 Winner System: Skill-based Conversational Agent with Supervised Dialog Manager (C18-1)
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| Challenge: | a dialog system developed for the 1st NIPS Conversational Intelligence Challenge 2017 (ConvAI) aims to train a bot capable of conversing with humans based on a given passage of text. |
| Approach: | They propose to implement a bot capable of conversing with humans based on a given passage of text. |
| Outcome: | The proposed bot won the 1st NIPS Conversational Intelligence Challenge 2017 (ConvAI) the system has been trained in a supervised setting using a dialogue manager . |
Decoupled Vocabulary Learning Enables Zero-Shot Translation from Unseen Languages (2024.acl-long)
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| Challenge: | Multilingual neural machine translation systems learn to map sentences of different languages into a common representation space. |
| Approach: | They propose a setup where we decouple learning of vocabulary and syntax and train to translate while keeping those word representations frozen. |
| Outcome: | The proposed setup achieves near parity with a supervised setting on the TED domain with varying number of languages seen by the encoder. |
Unsupervised Data Augmentation with Naive Augmentation and without Unlabeled Data (2021.emnlp-main)
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| Challenge: | Unsupervised Data Augmentation (UDA) is a semisupervised learning method that penalizes differences between a model's predictions on unlabeled examples and corresponding 'noised' examples produced via data augmentation. |
| Approach: | They propose to use a consistency loss to penalize differences between models' predictions on unlabeled and unlabed examples to enforce consistency between models and their perturbed counterparts. |
| Outcome: | The proposed method is able to penalize differences between models' outputs on unlabeled and unlabed examples without complex data augmentation. |
A Span-based Multimodal Variational Autoencoder for Semi-supervised Multimodal Named Entity Recognition (2022.emnlp-main)
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| Challenge: | Existing methods for named entity recognition on social media are not efficient for semi-supervised MNER because of the mismatch between the posted text and image. |
| Approach: | They propose a novel method to fuse the text and image features for multimodal named entity recognition under semi-supervised setting by exploiting modal-specific VAEs. |
| Outcome: | The proposed method outperforms baselines under supervised setting and improves performance with less labeled data than existing semi-supervised methods. |
Crowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognition (2021.acl-long)
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| Challenge: | Experimental results show that crowdsourced annotations are highly effective under supervised conditions. |
| Approach: | They propose an annotator-aware representation learning model that is inspired by domain adaptation methods which attempt to capture effective domain-alike features. |
| Outcome: | The proposed model is highly effective on a benchmark dataset and achieves state-of-the-art performance with only a very small scale of expert annotations. |
Efficient Zero-shot Event Extraction with Context-Definition Alignment (2022.findings-emnlp)
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| Challenge: | Conventional supervised methods cannot generalize to event types out of the pre-defined ontology. |
| Approach: | They propose to use two separate transformer models to model the definition semantics of an event type name into the same embedding space and then minimize their embeddable distance via contrastive learning. |
| Outcome: | The proposed model outperforms all previous zero-shot EE methods with fast inference speed due to the disjoint design. |
LM-Critic: Language Models for Unsupervised Grammatical Error Correction (2021.emnlp-main)
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| Challenge: | Recent work casts GEC as a translation problem using encoder-decoder models to map bad (ungrammatical) sentences into good (grammatically) sentences. |
| Approach: | They propose to use a pretrained language model to define an LM-Critic that judges a sentence to be grammatical if the LM assigns it a higher probability than its local perturbations. |
| Outcome: | The proposed method outperforms existing methods in both the unsupervised and supervised setting. |
PCMID: Multi-Intent Detection through Supervised Prototypical Contrastive Learning (2023.findings-emnlp)
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| Challenge: | Existing approaches to intent detection assume that each utterance represents only a single intent. |
| Approach: | They propose a framework for intent detection that can learn multiple representations of a given user utterance under the context of different intent labels in an optimized semantic space. |
| Outcome: | The proposed framework achieves state-of-the-art on multiple public benchmark datasets and a private real-world dataset for the multi-intent detection task. |
Improved Pseudo Data for Machine Translation Quality Estimation with Constrained Beam Search (2023.emnlp-main)
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Xiang Geng, Yu Zhang, Zhejian Lai, Shuaijie She, Wei Zou, Shimin Tao, Hao Yang, Jiajun Chen, Shujian Huang
| Challenge: | evaluating the quality of machine translation outputs becomes increasingly essential with the rapid development of machine language (MT). |
| Approach: | They propose to generate pseudo data using the MT model with constrained beam search (CBSQE) they propose to preserve the reference parts with high MT probabilities as correct translations . |
| Outcome: | The proposed model outperforms strong baselines in both supervised and unsupervised settings. |
UniEX: An Effective and Efficient Framework for Unified Information Extraction via a Span-extractive Perspective (2023.acl-long)
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| Challenge: | Existing approaches for information extraction (IE) are limited by the number of subtasks and the isolation of the subtask. |
| Approach: | They propose a new paradigm for universal information extraction that is compatible with any schema format and applicable to a list of IE tasks. |
| Outcome: | The proposed framework outperforms generative universal IE models on 14 benchmarks with the supervised setting and the state-of-the-art performance in low-resource scenarios. |
Hop, Union, Generate: Explainable Multi-hop Reasoning without Rationale Supervision (2023.emnlp-main)
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| Challenge: | Existing methods rely on supervision for both answers and rationales, but they have limited capacities in modeling interactions between sentences, let alone reasoning across multiple documents. |
| Approach: | They propose a principled, probabilistic approach for training explainable multi-hop question answering systems without rationale supervision. |
| Outcome: | The proposed method is more accurate at selecting rationales than previous methods while maintaining similar accuracy in predicting answers. |