Papers with supervised methods
CALOR-QUEST : generating a training corpus for Machine Reading Comprehension models from shallow semantic annotations (D19-58)
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Frederic Bechet, Cindy Aloui, Delphine Charlet, Geraldine Damnati, Johannes Heinecke, Alexis Nasr, Frederic Herledan
| Challenge: | Recent large corpora of triplets have opened the door to supervised machine learning approaches for Question-Answering. |
| Approach: | They propose to generate questions from the semantic Frame analysis of large corpora using a CALOR-QUEST resource in French and use it to improve machine reading comprehension. |
| Outcome: | The proposed method generates questions from the semantic Frame analysis of large corpora and then tests them on the CALOR-QUEST resource in French. |
Self-Supervised Knowledge Triplet Learning for Zero-Shot Question Answering (2020.emnlp-main)
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| Challenge: | Current supervised Question Answering methods rely on expensive data annotations and can introduce unintended annotator bias. |
| Approach: | They propose a self-supervised task over knowledge graphs that can be supervised by a data annotation tool. |
| Outcome: | The proposed task performs better than pre-trained language models on a large dataset. |
Utilizing Language-Image Pretraining for Efficient and Robust Bilingual Word Alignment (2022.findings-emnlp)
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Tuan Dinh, Jy-yong Sohn, Shashank Rajput, Timothy Ossowski, Yifei Ming, Junjie Hu, Dimitris Papailiopoulos, Kangwook Lee
| Challenge: | Recent studies show that unsupervised word translation is more accurate and robust without parallel corpora. |
| Approach: | They propose a method for unsupervised word translation that leverages visual observations and pretrained language-image models to align words. |
| Outcome: | The proposed method improves on the state-of-the-art language-image pretraining method for bilingual word alignment. |
Comprehensive Supersense Disambiguation of English Prepositions and Possessives (P18-1)
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Nathan Schneider, Jena D. Hwang, Vivek Srikumar, Jakob Prange, Austin Blodgett, Sarah R. Moeller, Aviram Stern, Adi Bitan, Omri Abend
| Challenge: | Frequent prepositions like for are maddeningly polysemous, their interpretation depends especially on the object of the preposition. |
| Approach: | They propose a new annotation scheme, corpus, and task for the disambiguation of prepositions and possessives in English. |
| Outcome: | The proposed annotations are comprehensive with respect to types and tokens of these markers and use broadly applicable supersense classes rather than fine-grained dictionary definitions. |
Unsupervised KB-to-Text Generation with Auxiliary Triple Extraction using Dual Learning (2020.aacl-main)
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| Challenge: | Existing methods to generate text from KB triples are limited and expensive . a novel approach is proposed to train the generation model in unsupervised way . |
| Approach: | They propose a method which trains the generation model in a completely unsupervised way with unaligned raw text data and KB triples. |
| Outcome: | The proposed method outperforms existing methods and is cost-effective. |
BPID: A Benchmark for Personal Identity Deduplication (2024.emnlp-industry)
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Runhui Wang, Yefan Tao, Adit Krishnan, Luyang Kong, Xuanqing Liu, Yuqian Deng, Yunzhao Yang, Henrik Johnson, Andrew Borthwick, Shobhit Gupta, Aditi Gundlapalli, Davor Golac
| Challenge: | Data deduplication is a critical task in data management and mining, focused on consolidating duplicate records that refer to the same entity. |
| Approach: | They propose to use a dataset with 1,000,000 unlabeled synthetic PII profiles and a subset of 10,000 pairs curated and labeled as matches or non-matches. |
| Outcome: | The proposed datasets contain synthetic profiles built from publicly available sources that do not represent real individuals. |
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing (2021.acl-demo)
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Xiao Wang, Qin Liu, Tao Gui, Qi Zhang, Yicheng Zou, Xin Zhou, Jiacheng Ye, Yongxin Zhang, Rui Zheng, Zexiong Pang, Qinzhuo Wu, Zhengyan Li, Chong Zhang, Ruotian Ma, Zichu Fei, Ruijian Cai, Jun Zhao, Xingwu Hu, Zhiheng Yan, Yiding Tan, Yuan Hu, Qiyuan Bian, Zhihua Liu, Shan Qin, Bolin Zhu, Xiaoyu Xing, Jinlan Fu, Yue Zhang, Minlong Peng, Xiaoqing Zheng, Yaqian Zhou, Zhongyu Wei, Xipeng Qiu, Xuanjing Huang
| Challenge: | Existing approaches to textual robustness evaluation focus on slightly modifying the input data, which maintains the original meaning and results in a different prediction. |
| Approach: | They propose a multilingual robustness evaluation toolkit for NLP that integrates universal text transformations, task-specific transformations and adversarial attack. |
| Outcome: | The toolkit includes universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analyses. |
Zero-shot Event Extraction via Transfer Learning: Challenges and Insights (2021.acl-short)
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| Challenge: | Existing methods for event extraction require expensive annotation and are not extensible to new event ontologies. |
| Approach: | They propose to use textual entailment and/or question answering queries to extract a zero-shot event from a set of TE and/ or QA queries. |
| Outcome: | The proposed method achieves acceptable results on ACE-2005 and ERE, but there is still a large gap from supervised approaches. |
Back to the Future: Unsupervised Backprop-based Decoding for Counterfactual and Abductive Commonsense Reasoning (2020.emnlp-main)
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Lianhui Qin, Vered Shwartz, Peter West, Chandra Bhagavatula, Jena D. Hwang, Ronan Le Bras, Antoine Bosselut, Yejin Choi
| Challenge: | Existing methods for integrating past and future contexts are limited and require manual input. |
| Approach: | They propose an unsupervised decoding algorithm that incorporates past and future contexts using off-the-shelf, left-to-right language models and no supervision. |
| Outcome: | The proposed method outperforms unsupervised methods on abductive and counterfactual reasoning tasks. |
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. |
MoqaGPT : Zero-Shot Multi-modal Open-domain Question Answering with Large Language Model (2023.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) like GPT-4 are not able to handle multi-modal open-domain question answering in a zero-shot manner. |
| Approach: | MoqaGPT uses divide-and-conquer strategy to extract answers from each modality separately. |
| Outcome: | MoqaGPT improves on MMCoQA dataset by +37.91 points and EM by +34.07 points. |
Self-Supervised Test-Time Learning for Reading Comprehension (2021.naacl-main)
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| Challenge: | Recent work on unsupervised question answering shows that models can be trained with procedurally generated question-answer pairs and achieve performance competitive with supervised methods. |
| Approach: | They propose a method that performs "test-time learning" on a given context . they use self-supervision to train models on synthetically generated question-answer pairs . |
| Outcome: | The proposed method outperforms current unsupervised methods and outperformed supervised methods. |
Query Enhanced Knowledge-Intensive Conversation via Unsupervised Joint Modeling (2023.acl-long)
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| Challenge: | Existing methods to retrieve knowledge-intensive conversations are based on external resources such as Wikipedia databases or search engine results. |
| Approach: | They propose an unsupervised query enhanced approach for knowledge-intensive conversations . they conduct experiments on three knowledge- intensive conversation datasets . |
| Outcome: | The proposed approach performs better than all unsupervised methods across three datasets and achieves competitive performance compared to supervised methods. |
TISE: A Tripartite In-context Selection Method for Event Argument Extraction (2024.naacl-long)
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| Challenge: | Recent studies show that LLMs can finish inference by providing several examples. |
| Approach: | They propose a method which integrates three requirements when selecting an in-context example and integrates them into a set of determinantal point processes to enhance the reasoning capabilities of LLMs. |
| Outcome: | The proposed method can achieve superior performance with fewer examples and outperform some supervised methods. |
Commonsense Knowledge Mining from Pretrained Models (D19-1)
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| Challenge: | Several approaches have been proposed for training models for commonsense knowledge base completion (CKBC) due to the sparsity of training data. |
| Approach: | They propose a method for generating commonsense knowledge using a large, pre-trained bidirectional language model by transforming relational triples into masked sentences. |
| Outcome: | The proposed method outperforms models trained on held-out test sets on a held-up set, suggesting that it generalizes better than current supervised methods. |
An Unsupervised Sentence Embedding Method by Mutual Information Maximization (2020.emnlp-main)
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| Challenge: | Sentence BERT is inefficient for sentence-pair tasks as it needs to evaluate combinatorially many sentence pairs which is very time-consuming. |
| Approach: | They propose a lightweight extension on top of BERT and a self-supervised learning objective to derive meaningful sentence embeddings in an unsupervised manner. |
| Outcome: | The proposed method outperforms baselines on common semantic textual similarity tasks and downstream supervised tasks and achieves performance competitive with supervised methods on various tasks. |
RAMP: Retrieval and Attribute-Marking Enhanced Prompting for Attribute-Controlled Translation (2023.acl-short)
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| Challenge: | Attribute-controlled translation (ACT) is a subtask of machine translation that involves controlling stylistic or linguistic attributes (like formality and gender) of outputs. |
| Approach: | They propose a new approach to attribute-controlled translation that leverages multilingual language models to perform ACT in few-shot and zero-shot settings. |
| Outcome: | The proposed approach improves generation accuracy over the standard prompting approach in both zero-shot and few-shot settings. |
KnowDis: Knowledge Enhanced Data Augmentation for Event Causality Detection via Distant Supervision (2020.coling-main)
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| Challenge: | Existing methods of event causality detection use hand-labeled training data. |
| Approach: | They propose a framework for event causality detection that augments training data via distant supervision. |
| Outcome: | The proposed framework outperforms existing methods on two benchmark datasets . it outperformed previous methods by a large margin assisted with automatically labeled training data. |
KB-Plugin: A Plug-and-play Framework for Large Language Models to Induce Programs over Low-resourced Knowledge Bases (2024.emnlp-main)
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| Challenge: | Program induction (PI) is a promising paradigm for using knowledge bases (KBs) to help large language models answer complex knowledge-intensive questions. |
| Approach: | They propose a plug-and-play framework that enables large language models to induce programs over any low-resourced KB. |
| Outcome: | Experiments show that KB-Plugin outperforms SoTA low-resourced PI methods with 25x smaller backbone LLM on large-scale and domain-specific KBs and even approaches the performance of supervised methods. |
Leveraging Gloss Knowledge in Neural Word Sense Disambiguation by Hierarchical Co-Attention (D18-1)
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| Challenge: | Existing models for Word Sense Disambiguation use labeled data, but lack gloss knowledge. |
| Approach: | They propose a co-attention mechanism to generate co-dependent representations for context and gloss . they propose to incorporate gloss knowledge into neural networks for Word Sense Disambiguation . |
| Outcome: | The proposed model achieves state-of-the-art results on standard English all-words WSD datasets. |
Unsupervised Conversation Disentanglement through Co-Training (2021.emnlp-main)
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| Challenge: | Existing work on conversation disentanglement relies heavily on human annotations, which is expensive to obtain in practice. |
| Approach: | They propose to train a conversation disentanglement model without referencing human annotations . they use a message-pair classifier and a session classifier to retrieve local relations . |
| Outcome: | The proposed method achieves competitive performance compared to previous methods on a large movie dialogue dataset. |
Zero-Shot Event Detection Based on Ordered Contrastive Learning and Prompt-Based Prediction (2022.findings-naacl)
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| Challenge: | Existing zero-shot event detection methods do not work for unseen types . supervised methods require predefined event types or external tools . |
| Approach: | They propose a framework to detect events from unstructured text without annotating samples . they propose to use ordered contrastive learning and prompt-based prediction to identify trigger words . |
| Outcome: | The proposed model detects events more effectively and accurately than state-of-the-art methods. |
Unsupervised Neural Text Simplification (P19-1)
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| Challenge: | Existing unsupervised methods for text simplification are limited to unlabeled text . paper aims to improve the performance of unsupervised systems by incorporating labeled pairs . |
| Approach: | They propose to use unlabeled text to train a neural text simplification framework . they propose to add a pair of attentional-decoders to the framework to improve performance . |
| Outcome: | The proposed model outperforms existing supervised methods on public test data. |
Heuristically Informed Unsupervised Idiom Usage Recognition (D18-1)
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| Challenge: | Existing models for idiom usage recognition have failed to recognize usages without annotated examples. |
| Approach: | They propose an unsupervised method for recognizing the intended usages of idioms by using distributional semantics to identify literal usages. |
| Outcome: | The proposed method performs competitively against supervised methods. |
Low-resource Taxonomy Enrichment with Pretrained Language Models (2021.emnlp-main)
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| Challenge: | Taxonomies represent hierarchical relationships between terms or entities. |
| Approach: | They propose a framework for taxonomy enrichment in low-resource settings with pretrained language models as knowledge bases to compensate for the shortage of information. |
| Outcome: | The proposed framework predicts whether inputted term pairs have hierarchical relationships and leverages implicit knowledge from the LM to generate queries efficiently. |
Multi-Input Attention for Unsupervised OCR Correction (P18-1)
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| Challenge: | Existing methods for OCR correction are mostly supervised methods that correct recognition errors in a single output. |
| Approach: | They propose a sequence-to-sequence model with attention and a decoder with attention averaging to search for consensus among multiple sequences. |
| Outcome: | The proposed methods cut the character and word error rates nearly in half on single inputs and can rival supervised methods. |
From Alignment to Assignment: Frustratingly Simple Unsupervised Entity Alignment (2021.emnlp-main)
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| Challenge: | Existing methods for cross-lingual entity alignment rely on lexical matching and probability reasoning, but they inherit poor interpretability and low efficiency from neural networks. |
| Approach: | They propose a simple but effective unsupervised entity alignment method without neural networks that can be used to find the equivalent entities between crosslingual KGs. |
| Outcome: | Extensive experiments show that the proposed method beats advanced supervised methods across all datasets while having high efficiency, interpretability, and stability. |
Incorporating Glosses into Neural Word Sense Disambiguation (P18-1)
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| Challenge: | Existing neural networks for Word Sense Disambiguation rely on labeled data and lexical knowledge. |
| Approach: | They propose a gloss-augmented WSD neural network which integrates context and glosses of the target word into a unified framework. |
| Outcome: | The proposed model outperforms the state-of-the-art systems on several English all-words WSD datasets. |
Ambiguity Meets Uncertainty: Investigating Uncertainty Estimation for Word Sense Disambiguation (2023.findings-acl)
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| Challenge: | Existing supervised methods treat word sense disambiguation as a classification task but ignore uncertainty estimation (UE) in the real-world setting, the data is always noisy and out of distribution. |
| Approach: | They propose to use word sense disambiguation to determine an appropriate sense for a word given its context to determine the most appropriate sense. |
| Outcome: | The proposed model reflects data uncertainty satisfactorily but underestimates model uncertainty. |
A Unified Supervised and Unsupervised Dialogue Topic Segmentation Framework Based on Utterance Pair Modeling (2025.naacl-long)
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| Challenge: | Unsupervised methods for dialogue topic segmentation are difficult to surpass due to short sentences, serious references and non-standard language. |
| Approach: | They propose a method to divide a dialogue into different topic paragraphs to better understand its structure and content. |
| Outcome: | The proposed method achieves the best results on multiple benchmark datasets across different scenarios. |
MetaTS: Meta Teacher-Student Network for Multilingual Sequence Labeling with Minimal Supervision (2021.emnlp-main)
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| Challenge: | Sequence labeling aims to predict fine-grained sequences of labels for text, but lack of token-level annotated data hinders the effectiveness of supervised methods. |
| Approach: | They propose a Meta Teacher-Student (MetaTS) Network to alleviate data scarcity by leveraging large multilingual unlabeled data. |
| Outcome: | The proposed meta learning method alleviates data scarcity by leveraging large multilingual unlabeled data. |
CYCLE-INSTRUCT: Fully Seed-Free Instruction Tuning via Dual Self-Training and Cycle Consistency (2025.emnlp-main)
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Zhanming Shen, Hao Chen, Yulei Tang, Shaolin Zhu, Wentao Ye, Xiaomeng Hu, Haobo Wang, Gang Chen, Junbo Zhao
| Challenge: | Existing methods for instruction tuning rely on expensive human-annotated seed data or powerful external teacher models. |
| Approach: | They propose a framework that achieves fully seed-free instruction tuning by employing a dual self-training loop where two models are bootstrapped solely from raw, unlabeled text. |
| Outcome: | The proposed framework outperforms seed-driven back-translation baselines and achieves comparable performance to strongly supervised methods. |
Unsupervised Multilingual Word Embedding with Limited Resources using Neural Language Models (P19-1)
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| Challenge: | Existing methods that map word embeddings into a common space without any parallel data or pre-training have been proposed that are limited in resources and perform poorly under resource-poor conditions. |
| Approach: | They propose a model that maps monolingual word embeddings into a common space without any parallel data and generates multilingual embeddables without any pre-training. |
| Outcome: | The proposed model outperforms existing methods on word alignment tasks on low-resource conditions and with limited resources. |
DISCO: Distilling Counterfactuals with Large Language Models (2023.acl-long)
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| Challenge: | high-quality counterfactual data is scarce for most tasks and not easily generated at scale. |
| Approach: | They propose a method for automatically generating high-quality counterfactual data at scale . they use a large general language model to generate phrasal perturbations and filter them . |
| Outcome: | The proposed method is task-agnostic and can be applied to the task of natural language inference. |
MAAM: A Morphology-Aware Alignment Model for Unsupervised Bilingual Lexicon Induction (P19-1)
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| Challenge: | Existing work shows that morphological variation is an intractable challenge for the unsupervised bilingual lexicon induction task. |
| Approach: | They propose a morphology-aware alignment model to alleviate the adverse effect of morphological variation by introducing grammatical information learned by the pre-trained denoising language model. |
| Outcome: | The proposed model outperforms state-of-the-art unsupervised systems and achieves competitive performance compared to supervised methods. |
Preserving Zero-shot Capability in Supervised Fine-tuning for Multi-label Text Classification (2025.findings-naacl)
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| Challenge: | Existing methods that assume label descriptions ensure zero-shot capability lose their zero-shot capability during training. |
| Approach: | They propose a method that preserves the zero-shot capabilities of powerful dual encoders and label-wise attention networks by freezing the label encoder. |
| Outcome: | The proposed methods preserve the zero-shot capabilities of powerful dual encoder and label-wise attention network architectures by freezing the label encoder. |
GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge (D19-1)
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| Challenge: | Word Sense Disambiguation (WSD) aims to find the exact sense of an ambiguous word in a particular context. |
| Approach: | They propose to integrate gloss knowledge into supervised neural networks for Word Sense Disambiguation (WSD) this paper proposes to fine-tune a pre-trained BERT model and achieve new state-of-the-art results on WSD task. |
| Outcome: | The proposed model achieves state-of-the-art on the word Sense Disambiguation (WSD) task. |
Unsupervised Candidate Answer Extraction through Differentiable Masker-Reconstructor Model (2023.findings-emnlp)
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| Challenge: | Existing methods for candidate answer extraction are reliant on linguistic rules or annotated data and face partial annotation issue and challenges in generalization. |
| Approach: | They propose an unsupervised approach that leverages the inherent structure of context passages through a Differentiable Masker-Reconstructor (DMR) Model with the enforcement of self-consistency for picking up salient information tokens. |
| Outcome: | The proposed model outperforms supervised and unsupervised methods in two datasets with exhaustively-annotated answers and shows that it is comparable to supervised methods. |
A Named Entity Recognition Corpus for Vietnamese Biomedical Texts to Support Tuberculosis Treatment (2022.lrec-1)
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| Challenge: | Named Entity Recognition (NER) is an important task in information extraction. |
| Approach: | They construct a labelled NER corpus of Vietnamese academic biomedical text . they annotate documents with five categories of named entities: Organisation, Location, Date and Time, Symptom and Disease, and Diagnostic Procedure. |
| Outcome: | The proposed system could provide answers to questions related to TB in Vietnamese . the system could also be used to identify TB-related diseases in the country . |
Bootstrapped Unsupervised Sentence Representation Learning (2021.acl-long)
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| Challenge: | Existing approaches to learn sentence representations rely on quality labeled data. |
| Approach: | They propose a Siamese Network which maximizes similarity between two augmented views of each sentence. |
| Outcome: | The proposed method outperforms state-of-the-art methods on STS and classification tasks. |
AX-MABSA: A Framework for Extremely Weakly Supervised Multi-label Aspect Based Sentiment Analysis (2022.emnlp-main)
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| Challenge: | Aspect Based Sentiment Analysis is a dominant research area with potential applications in social media analytics, business, finance, and health. |
| Approach: | They propose a weakly supervised multi-label Aspect Category Sentiment Analysis framework which does not use any labelled data. |
| Outcome: | The proposed framework outperforms weakly supervised baselines on four benchmark datasets and is able to generate multiple aspect category-sentiment pairs per review sentence. |
Unsupervised Alignment-based Iterative Evidence Retrieval for Multi-hop Question Answering (2020.acl-main)
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| Challenge: | Evidence retrieval is a critical stage of question answering (QA) . Several multi-hop QA datasets have been proposed recently . |
| Approach: | They propose an unsupervised method that uses only GloVe embeddings to soft-align questions with justification sentences and an iterative process that reformulates queries focusing on terms that are not covered by existing justifications. |
| Outcome: | The proposed method outperforms all previous methods on the evidence selection task on two datasets: MultiRC and QASC. |
Label Augmentation for Zero-Shot Hierarchical Text Classification (2024.acl-long)
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| Challenge: | Hierarchical Text Classification is a difficult problem due to the lack of labeled data and the cost of manually annotating data samples. |
| Approach: | They propose a method that uses a Large Language Model to augment the deepest layer of the labels hierarchy to enhance its specificity. |
| Outcome: | The proposed method achieves state-of-the-art on four public datasets and a strong correlation between the metric values and the classification performance. |
Unsupervised Paraphrasing with Pretrained Language Models (2021.emnlp-main)
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| Challenge: | Paraphrase generation has benefited from recent advances in the design of training objectives and model architectures, but previous studies focused on supervised methods that require a large amount of labeled data that is costly to collect. |
| Approach: | They propose a transfer learning approach that enables pre-trained language models to generate high-quality paraphrases in an unsupervised setting. |
| Outcome: | The proposed model performs state-of-the-art on the Quora Question Pair and ParaNMT datasets and is robust to domain shift between the two datasets. |
DACSA: A large-scale Dataset for Automatic summarization of Catalan and Spanish newspaper Articles (2022.naacl-main)
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| Challenge: | a large corpus of documents is available for summarization tasks in English . supervised methods require adequate corpora for summarizing . |
| Approach: | They describe a corpus of catalan and spanish newspapers that can be used to train summarization models for Catalan, Spanish and other languages. |
| Outcome: | The proposed corpus can be used to train summarization models for Catalan and Spanish. |
CHARM: Inferring Personal Attributes from Conversations (2020.emnlp-main)
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| Challenge: | Personal Knowledge Bases (PKBs) capture individual user traits for customizing downstream applications like chatbots or recommenders. |
| Approach: | They propose a method that leverages keyword extraction and document retrieval to predict attribute values that were never seen during training. |
| Outcome: | The proposed method can predict attributes that were never seen during training. |
The Summary Loop: Learning to Write Abstractive Summaries Without Examples (2020.acl-main)
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| Challenge: | Unsupervised abstractive summarization is important for news headlines and research papers . a novel method that encourages the inclusion of key terms from the original document into the summary is presented . |
| Approach: | They propose a method that encourages the inclusion of key terms from the original document into the summary by a coverage model along with a fluency model. |
| Outcome: | The proposed method outperforms existing methods on news summarization datasets and is competitive with existing methods. |
Transferable End-to-End Aspect-based Sentiment Analysis with Selective Adversarial Learning (D19-1)
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| Challenge: | Existing methods to extract aspects and sentiments are limited due to lack of annotated sequence data. |
| Approach: | They propose a Selective Adversarial Learning method to align latent correlation vectors . they propose tagging a set of aspect boundary tags and sentiment tags to create a joint label space . |
| Outcome: | The proposed method can learn weights for words to achieve fine-grained adaptation. |
Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLP (2021.emnlp-main)
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| Challenge: | Meta-learning considers learning as an efficient learning process that can leverage its past experience to accurately solve new tasks. |
| Approach: | They propose to provide task distributions for meta-learning by considering self-supervised tasks automatically proposed from unlabeled text to enable large-scale meta- learning in NLP. |
| Outcome: | The proposed distributions show that human learning models perform better on the few-shot benchmark than previous methods. |
ConGen: Unsupervised Control and Generalization Distillation For Sentence Representation (2022.findings-emnlp)
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Peerat Limkonchotiwat, Wuttikorn Ponwitayarat, Lalita Lowphansirikul, Can Udomcharoenchaikit, Ekapol Chuangsuwanich, Sarana Nutanong
| Challenge: | Sentence representations are essential in many NLP tasks operating at the sentence level. |
| Approach: | They propose an unsupervised sentence representation method to reduce the supervised-unsupervised performance gap for smaller models. |
| Outcome: | The proposed method outperforms supervised training on STS, text classification, and natural language inference tasks on smaller models. |
SAEs Are Good for Steering – If You Select the Right Features (2025.emnlp-main)
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| Challenge: | Sparse Autoencoders (SAEs) can learn a decomposition of a model’s latent space by analyzing the input tokens that activate them. |
| Approach: | They propose an unsupervised approach to learn a decomposition of a model’s latent space by analyzing the input tokens that activate them. |
| Outcome: | The proposed approach matches the performance of existing supervised methods by identifying features with low output scores and identifying them with input and output scores. |
Enhancing Phrase Representation by Information Bottleneck Guided Text Diffusion Process for Keyphrase Extraction (2024.lrec-main)
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| Challenge: | Existing methods for keyphrase extraction lack the ability to utilize keyphrase information, which may result in biased results. |
| Approach: | They propose a keyphrase extraction task that leverages the supervised Variational Information Bottleneck to guide the text diffusion process for generating enhanced keyphrase representations. |
| Outcome: | The proposed keyphrase extraction model outperforms existing methods on open domain keyphrase extractor benchmark and scientific domain dataset. |
Unsupervised Text Style Transfer with Padded Masked Language Models (2020.emnlp-main)
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| Challenge: | Existing methods for style transfer are difficult to obtain and require substantial amounts of parallel training examples to work well. |
| Approach: | They propose an unsupervised method for style transfer that uses masked language models to find the text spans where the two models disagree the most in terms of likelihood. |
| Outcome: | The proposed method performs competitively in a fully unsupervised setting and improves accuracy in low-resource settings by over 10 percentage points when pre-training on silver training data generated by Masker. |
Demonstrations Are All You Need: Advancing Offensive Content Paraphrasing using In-Context Learning (2024.findings-acl)
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| Challenge: | Paraphrasing of offensive content is a better alternative to content removal, but supervised methods often retain a large portion of the offensiveness of the original content. |
| Approach: | They propose to use In-Context Learning (ICL) to generate usable offensive paraphrases by using large language models. |
| Outcome: | The proposed framework is better than supervised methods on human evaluation and lower toxicity by 76%. |
VaccineLies: A Natural Language Resource for Learning to Recognize Misinformation about the COVID-19 and HPV Vaccines (2022.lrec-1)
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| Challenge: | VaccineLies can detect misinformation about vaccines on Twitter without using language resources. |
| Approach: | They present a dataset of tweets propagating misinformation about two vaccines . authors propose novel methods to detect misinformation on Twitter and identify stance towards it . |
| Outcome: | VaccineLies can detect misinformation on Twitter and identify the stance towards it. |
MolRAG: Unlocking the Power of Large Language Models for Molecular Property Prediction (2025.acl-long)
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| Challenge: | Recent LLMs exhibit limited effectiveness on molecular property prediction task due to semantic gap between representations and natural language and lack of domain-specific knowledge. |
| Approach: | They propose a framework that integrates Chain-of-Thought reasoning for molecular property prediction. |
| Outcome: | The proposed framework outperforms pre-trained LLMs on four datasets and matches supervised methods. |
Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents (2023.emnlp-main)
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Weiwei Sun, Lingyong Yan, Xinyu Ma, Shuaiqiang Wang, Pengjie Ren, Zhumin Chen, Dawei Yin, Zhaochun Ren
| Challenge: | Existing work utilizes generative LLMs for Information Retrieval (IR) rather than direct passage ranking. |
| Approach: | They investigate generative LLMs such as ChatGPT and GPT-4 for relevance ranking in IR and use a test set to verify the model’s ability to rank unknown knowledge. |
| Outcome: | The proposed model outperforms a 3B supervised model on the BEIR benchmark. |
Unsupervised Word-level Quality Estimation for Machine Translation Through the Lens of Annotators (Dis)agreement (2025.emnlp-main)
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| Challenge: | Modern WQE techniques rely on expensive inference with large language models or ad-hoc training with large amounts of human-labeled data. |
| Approach: | They propose to use word-level quality estimation to identify translation errors from the inner workings of translation models to quantify the impact of human label variation on metric performance. |
| Outcome: | The proposed methods identify translation errors from the inner workings of translation models using human labels. |
Zero-Shot Multimodal Retrieval with Multi-Scale Contextual Representations (2026.acl-long)
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| Challenge: | Existing approaches to multimodal information retrieval (MMIR) lack generalization across different modalities and require annotated training data. |
| Approach: | They propose a fine-tuning-free, two-stage MMIR approach that couples efficient candidate filtering with fine-grained multimodal re-ranking. |
| Outcome: | The proposed approach outperforms supervised methods on 23 datasets. |
Label Confidence Weighted Learning for Target-level Sentence Simplification (2024.emnlp-main)
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| Challenge: | Existing methods for sentence simplification use label confidence weighting to generate pseudo-labeled sentences with varying proficiency levels. |
| Approach: | They propose a label confidence weighting scheme for multi-level sentence simplification that incorporates a weighting system into the training loss of the encoder-decoder model. |
| Outcome: | The proposed approach outperforms state-of-the-art confidence weighting methods on English grade-level simplification datasets. |
Targeting the Needle, Ignoring the Haystack: Anchoring Crucial Cues for Evolving Scam Call Detection via an LLM-Assisted Classifier (2026.findings-acl)
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| Challenge: | Existing methods for fraud detection on online service platforms often fail to generalize due to the scarcity of labeled data and the continuous evolution of conversational contexts. |
| Approach: | They propose a framework that anchors detection on Semantic Primitives . they prioritize stable evidence over conversational noise to ensure a verifiable fraud tactic . |
| Outcome: | The proposed framework achieves superior robustness and efficiency compared to baselines . it prioritizes stable evidence over diverse conversational noise . |
Unsupervised Grouping of Public Procurement Similar Items: Which Text Representation Should I Use? (2024.lrec-main)
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Pedro P. V. Brum, Mariana O. Silva, Gabriel P. Oliveira, Lucas G. L. Costa, Anisio Lacerda, Gisele Pappa
| Challenge: | establishing reference prices is essential to guide competitors in setting product prices . however, selecting an appropriate representation for text is challenging . |
| Approach: | They propose a framework for text cleaning, extraction, and representation based on sentence representations for public procurement item descriptions. |
| Outcome: | The proposed approach captures the most important components of item descriptions. |
Who Said What: Formalization and Benchmarks for the Task of Quote Attribution (2024.lrec-main)
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| Challenge: | Existing methods for quote attribution are poorly understood, despite advances in research . previous approaches have used hand-crafted features to identify speaker names . |
| Approach: | They formalize the task of quote attribution and establish a basis for comparison . they compare CEQA and ChatGPT models on available datasets in both English and Chinese . |
| Outcome: | The proposed model outperforms all supervised methods on English and Chinese datasets. |