Papers with unsupervised methods
More than just Frequency? Demasking Unsupervised Hypernymy Prediction Methods (2021.findings-acl)
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| Challenge: | Using unsupervised methods of hypernymy prediction, we show that the predictions of three methods overlap and are highly correlated with frequency-based predictions. |
| Approach: | They compare unsupervised methods of hypernymy prediction to supervised methods . they show that the methods overlap and are highly correlated with frequency-based predictions . |
| Outcome: | The proposed methods overlap and are highly correlated with frequency-based predictions across English and German datasets. |
Learning Domain Terms - Empirical Methods to Enhance Enterprise Text Analytics Performance (2020.coling-industry)
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| Challenge: | Standard text analytics algorithms are degraded on consumer generated data, a study finds . enterprise data contains domain terms that are not available in general purpose thesaurus . |
| Approach: | They propose unsupervised methods for cleaning noisy repositories after domain terms are segregated from true noise terms. |
| Outcome: | The proposed method improves classification accuracy after noise correction . it is based on a systematic analysis of enterprise data . |
Unsupervised Paraphrasing by Simulated Annealing (2020.acl-main)
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| Challenge: | Existing approaches to generate accurate and different-appearing paraphrases require massive parallel samples for training. |
| Approach: | They propose a novel approach that accomplishes Unsupervised Paraphrasing by Simulated Annealing by performing local editing. |
| Outcome: | The proposed approach outperforms existing models in automatic and human evaluations on Quora, Wikianswers, MSCOCO, and Twitter. |
Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection (N18-1)
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| Challenge: | Existing unsupervised methods for learning hypernyms from unlabeled text are not scaled to large vocabularies or yield unacceptably poor accuracy. |
| Approach: | They propose an unsupervised method of hypernym discovery using word contexts . they use word2vec to embed word context distributions without supervision . |
| Outcome: | The proposed method provides double the precision and highest average performance on 11 datasets. |
Unsupervised Bitext Mining and Translation via Self-Trained Contextual Embeddings (2020.tacl-1)
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| Challenge: | Existing methods to extract parallel sentences from unaligned text yield surprisingly good results. |
| Approach: | They propose an unsupervised method to create pseudo-parallel corpora for machine translation (MT) from unaligned text using multilingual BERT to create source and target sentence embeddings for nearest-neighbor search and adapt the model via self-training. |
| Outcome: | The proposed method outperforms existing methods and outperformed previous state-of-the-art methods by boosting translation performance by up to 3.5 BLEU on the WMT’14 French-English and WMT'16 German-English tasks. |
Can ChatGPT’s Performance be Improved on Verb Metaphor Detection Tasks? Bootstrapping and Combining Tacit Knowledge (2024.acl-long)
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| Challenge: | Existing methods for supervised metaphor detection are limited by their performance. |
| Approach: | They propose to use ChatGPT to detect most prevalent verb metaphors among metaphors . they use literal collocations of target verbs and subject-object pairs of verbs to detect them . |
| Outcome: | The proposed method achieves the best performance on the unsupervised verb metaphors detection task compared to existing unsupervised methods or direct prediction using ChatGPT. |
From Disjoint Sets to Parallel Data to Train Seq2Seq Models for Sentiment Transfer (2020.findings-emnlp)
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| Challenge: | Existing methods for sentiment transfer have relied on unsupervised methods due to lack of parallel corpora. |
| Approach: | They propose a method for creating parallel data to train Seq2Seq neural networks for sentiment transfer. |
| Outcome: | The proposed method outperforms existing unsupervised methods in sentiment transfer tasks. |
Unsupervised Single Document Abstractive Summarization using Semantic Units (2022.aacl-main)
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| Challenge: | a lack of sufficient training pairs is a common issue in real-world applications. |
| Approach: | They propose a framework that lets a model learn the frequency of each semantic unit in the source text. |
| Outcome: | The proposed model outperforms other unsupervised methods under CNN/Daily Mail task. |
Keyphrase Generation: A Text Summarization Struggle (N19-1)
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| Challenge: | Existing methods for keyphrase generation are unable to produce valuable terms that do not appear in the text. |
| Approach: | They propose to consider the keyphrase string as an abstractive summary of the title and the abstract. |
| Outcome: | The proposed method can generate better keyphrases than the existing methods or the unsupervised ones. |
Text Segmentation as a Supervised Learning Task (N18-2)
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| Challenge: | Existing datasets for text segmentation are small in size and do not represent the natural distribution of text in documents. |
| Approach: | They propose a large dataset for text segmentation that is automatically extracted and labeled from Wikipedia and develop a model based on this dataset. |
| Outcome: | The proposed model generalizes well to unseen natural text. |
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. |
Leveraging Only the Category Name for Aspect Detection through Prompt-based Constrained Clustering (2022.findings-emnlp)
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| Challenge: | Aspect category detection (ACD) aims to automatically identify user-concerned aspects from online reviews. |
| Approach: | They propose a method that relies on the category name of each aspect and a pretrained language model to generate constraints for clustering. |
| Outcome: | The proposed framework performs better than existing weakly supervised methods on nine benchmark datasets. |
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. |
Unsupervised Pronoun Resolution via Masked Noun-Phrase Prediction (2021.acl-short)
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| Challenge: | Masked Noun-Phrase Prediction (MNPP) is a pre-training strategy to tackle pronoun resolution in a fully unsupervised setting. |
| Approach: | They propose a pre-training strategy to tackle pronoun resolution in an unsupervised setting by fine-tuning a large pre-trained model on a human-labeled dataset and then transferring to a smaller dataset such as Winograd Schema Challenge (WSC). |
| Outcome: | The proposed method outperforms all previous unsupervised methods on all datasets by large margins. |
Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models (N19-1)
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| Challenge: | Existing approaches to define action spaces for conversational agents have limitations . end-to-end dialog systems can handle complex domains with limited action space . |
| Approach: | They propose a latent action framework that treats the action spaces of an end-to-end dialog agent as latent variables and develops unsupervised methods to induce its own action space from the data. |
| Outcome: | The proposed framework achieves better performance than word-level policy gradient methods on DealOrNoDeal and MultiWoz dialogs. |
Casting Light on Invisible Cities: Computationally Engaging with Literary Criticism (N19-1)
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| Challenge: | Literary critics often attempt to uncover meaning in a single work of literature through careful reading and analysis. |
| Approach: | They propose to use a literary theory to analyze Italo Calvino's novel Invisible Cities to leverage contextualized representations to embed each city's description and use unsupervised methods to cluster embeddings. |
| Outcome: | The proposed method can be applied to Italo Calvino’s novel Invisible Cities . authors compare results to similarity judgments generated by human readers . |
Unsupervised Deep Language and Dialect Identification for Short Texts (2020.coling-main)
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| Challenge: | Existing methods for identifying closely related short texts are unsupervised . however, performance is poor for unsupervised methods for short texts . |
| Approach: | They propose a method which can learn sentence embeddings and cluster assignments from short texts. |
| Outcome: | The proposed method outperforms state-of-the-art methods in supervised settings . it can learn sentence embeddings and cluster assignments from short texts . |
Modeling Event Salience in Narratives via Barthes’ Cardinal Functions (2020.coling-main)
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| Challenge: | Existing methods for estimating event salience without annotations are prohibitively costly because they require annotators to understand the concept of event salientity. |
| Approach: | They propose to use Barthes’ definition of event salience to compute event salientity without annotations by using a pre-trained language model. |
| Outcome: | The proposed methods outperform baseline methods on folktales with event salience annotation and fine-tuned language model is key factor in improving the methods. |
Gromov-Wasserstein Alignment of Word Embedding Spaces (D18-1)
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| Challenge: | Current unsupervised methods for learning cross-lingual correspondences involve multiple steps, including heuristic post-hoc refinement strategies. |
| Approach: | They propose to cast the correspondence problem directly as an optimal transport problem, building on the idea that word embeddings arise from metric recovery algorithms. |
| Outcome: | The proposed method can be estimated efficiently, requires little or no tuning, and performs comparable with the state-of-the-art in various unsupervised word translation tasks. |
Empirical Study of Zero-shot Keyphrase Extraction with Large Language Models (2025.coling-main)
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| Challenge: | a prompting-based approach can effectively supersede traditional KE methods, a study shows . our code is available at https://github.com/kangnlp/zero-shot-keyphrase-extraction-with-LLMs. |
| Approach: | They propose four prompting strategies for zero-shot keyphrase extraction using Large Language Models. |
| Outcome: | The proposed prompting strategies outperform state-of-the-art prompting methods on KE benchmark datasets. |
TAGCOS: Task-agnostic Gradient Clustered Coreset Selection for Instruction Tuning Data (2025.findings-naacl)
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| Challenge: | Existing methods for instruction tuning are limited due to the increasing volume of instruction datasets and the increased computational costs. |
| Approach: | They propose to extract a small and highly informative subset of training samples from a large dataset that achieves comparable performance to the full dataset. |
| Outcome: | The proposed algorithm outperforms other unsupervised methods and achieves comparable performance to the full dataset. |
Non-contrastive sentence representations via self-supervision (2024.findings-naacl)
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| Challenge: | Text embeddings are an important tool for a variety of NLP tasks. |
| Approach: | They compare sample contrastive methods with the standard baseline for contrastive sentence embeddings, SimCSE, and a class of self-supervised non-contrastive loss functions and methods. |
| Outcome: | The proposed methods outperform the standard baseline for contrastive sentence embeddings, SimCSE, on downstream tasks without auxiliary loss functions. |
Self-Supervised Quality Estimation for Machine Translation (2021.emnlp-main)
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Yuanhang Zheng, Zhixing Tan, Meng Zhang, Mieradilijiang Maimaiti, Huanbo Luan, Maosong Sun, Qun Liu, Yang Liu
| Challenge: | Training QE models require massive parallel data with hand-crafted quality annotations, which are time-consuming and labor-intensive to obtain. |
| Approach: | They propose a self-supervised method to evaluate machine-translated sentences without references by recovering masked target words. |
| Outcome: | The proposed method outperforms previous unsupervised methods on several QE tasks in different language pairs and domains. |
A Relaxed Matching Procedure for Unsupervised BLI (2020.acl-main)
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| Challenge: | Recent studies have shown that unsupervised bilingual lexicon induction is even on par with supervised methods. |
| Approach: | They propose a relaxed matching procedure to find a more precise matching between two languages by aligning source and target embedding space bidirectionally. |
| Outcome: | The proposed method significantly outperforms previous unsupervised methods on standard benchmarks. |
UCoder: Unsupervised Code Generation by Internal Probing of Large Language Models (2026.findings-acl)
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Jiajun Wu, Jian Yang, Wei Zhang, Linzheng Chai, Yuchi Ma, Ensheng Shi, Yuqing Ma, Zhoujun Li, Xianglong Liu
| Challenge: | Large language models (LLMs) have demonstrated remarkable capabilities in code generation tasks, but their effectiveness relies on supervised training with extensive labeled data and computational resources. |
| Approach: | They propose an unsupervised method that leverages Internal Probing of Large language models for Code generation without any external corpus, even unlabeled code snippets. |
| Outcome: | The proposed method can achieve competitive performance compared to supervised approaches while reducing the dependency on labeled data and computational resources. |
Uncovering Visual-Semantic Psycholinguistic Properties from the Distributional Structure of Text Embedding Space (2025.acl-long)
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| Challenge: | Imageability and concreteness are psycholinguistic properties that link visual and semantic spaces. |
| Approach: | They propose an unsupervised measure that quantifies sharpness of peaks in an image-caption dataset. |
| Outcome: | The proposed method is more robust than existing methods and predicts these properties for classification. |
SelfORE: Self-supervised Relational Feature Learning for Open Relation Extraction (2020.emnlp-main)
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| Challenge: | Existing methods for relation extraction use heuristics or distant-supervised annotations, but distant supervised methods make strong assumptions on entity cooccurrence without sufficient contexts. |
| Approach: | They propose a framework that exploits weak, self-supervised signals by leveraging large pretrained language models for adaptive clustering on contextualized relational features. |
| Outcome: | The proposed framework exploits weak, self-supervised signals on open-domain Relation Extraction . it bootstraps the self-supervised signals by improving contextualized features in relation classification . |
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. |
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. |
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. |
Chinese Spelling Corrector Is Just a Language Learner (2024.findings-acl)
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| Challenge: | a recent study shows that self-supervised learning can improve Chinese spelling correction by removing errors from training data. |
| Approach: | They propose a method that decodes Chinese spelling correction models using noise . they say it outperforms current methods that rely on annotated errors . |
| Outcome: | The proposed method outperforms the confusion set in specific domains because there are no errors in the training data. |
Uncovering Latent Arguments in Social Media Messaging by Employing LLMs-in-the-Loop Strategy (2025.findings-naacl)
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| Challenge: | Supervised methods are adept at text categorization, but dynamic nature of social media debates pose challenges for them . traditional methods for extracting themes from public discourse often reveal overarching patterns that might not capture specific nuances. |
| Approach: | They propose a generic approach that leverages the advanced capabilities of Large Language Models to extract latent arguments from social media messaging. |
| Outcome: | The proposed approach leverages the advanced capabilities of Large Language Models (LLMs) to extract latent arguments from social media messaging. |
Learning to Pronounce Chinese Without a Pronunciation Dictionary (2020.emnlp-main)
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| Challenge: | EM method achieves a test-set accuracy of 71%, vector-based method achieve 81%. |
| Approach: | They propose a program that learns to pronounce Chinese text in Mandarin without a pronunciation dictionary. |
| Outcome: | The proposed program deciphers Chinese text in Mandarin without a pronunciation dictionary. |
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. |
ParaMac: A General Unsupervised Paraphrase Generation Framework Leveraging Semantic Constraints and Diversifying Mechanisms (2022.findings-emnlp)
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| Challenge: | Existing unsupervised methods for paraphrase generation are weak in semantic equivalence or expression diversity. |
| Approach: | They propose a framework for unsupervised paraphrase generation that employs multi-aspect equivalence constraints and multi-granularity diversifying mechanisms to achieve good semantic equvalence and expressive diversity. |
| Outcome: | The proposed framework achieves 9.1% and 3.3% absolute gains over previous SOTA on Quora and MSCOCO and can improve to 18.0% and 4.6% on GLUE. |
Generate, Discriminate and Contrast: A Semi-Supervised Sentence Representation Learning Framework (2022.emnlp-main)
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| Challenge: | Existing supervised sentence embedding techniques rely on expensive human-annotated sentence pairs as the supervised signals. |
| Approach: | They propose a semi-supervised sentence embedding framework that leverages large-scale unlabeled data. |
| Outcome: | The proposed framework surpasses state-of-the-art methods on four domain adaptation tasks. |
Word2Sense: Sparse Interpretable Word Embeddings (P19-1)
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| Challenge: | Word2Sense embeddings are interpretable, but they are sparse and fast to compute . a unitary rotation can be applied to many of these embeddables retaining their utility for computational tasks while changing the values of individual coordinates. |
| Approach: | They propose an unsupervised method to generate Word2Sense word embeddings that are interpretable. |
| Outcome: | The proposed method compares well with other unsupervised word embeddings on NLP tasks. |
Annotations Are Not All You Need: A Cross-modal Knowledge Transfer Network for Unsupervised Temporal Sentence Grounding (2023.findings-emnlp)
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| Challenge: | Existing work on temporal sentence grounding rely on expensive video-query paired annotations . despite this, there are no ground-truth annotations in the current work . |
| Approach: | They propose to use paired video-query and segment boundary annotations to generate temporal sentence grounding without training. |
| Outcome: | The proposed model outperforms existing unsupervised methods and beats supervised ones on two challenging datasets. |
Text Detoxification using Large Pre-trained Neural Models (2021.emnlp-main)
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David Dale, Anton Voronov, Daryna Dementieva, Varvara Logacheva, Olga Kozlova, Nikita Semenov, Alexander Panchenko
| Challenge: | Existing studies on text detoxification cast this task as style transfer . text detox requires better preservation of the original meaning, authors argue . |
| Approach: | They propose two unsupervised methods for eliminating toxicity in text . they use a paraphraser guided by style-trained language models to keep the text content . |
| Outcome: | The proposed methods yield new SOTA results. |
Paraphrasing Compound Nominalizations (2021.emnlp-main)
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| Challenge: | Nominalizations are difficult to interpret because of ambiguous semantic relations between deverbal noun and its arguments. |
| Approach: | They propose to generate clausal paraphrases for nominalizations by mapping arguments to verbs . they use a contextualized language model to re-rank nominalization candidates . |
| Outcome: | The proposed task is based on a pre-trained model to re-rank paraphrase candidates identified by a textual entailment model. |
ReadMe++: Benchmarking Multilingual Language Models for Multi-Domain Readability Assessment (2024.emnlp-main)
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| Challenge: | Existing evaluation resources lack domain and language diversity, limiting the ability for cross-domain and cross-lingual analyses. |
| Approach: | They propose to use a multilingual multi-domain dataset to benchmark multilingual and monolingual models for multilingual readability assessment. |
| Outcome: | The proposed model trains better in supervised, unsupervised, and few-shot prompting settings and identifies shortcomings in state-of-the-art unsupervised methods. |
HMCL: Task-Optimal Text Representation Adaptation through Hierarchical Contrastive Learning (2025.findings-emnlp)
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| Challenge: | Hierarchical Multilevel Contrastive Learning (HMCL) improves text representation for general large language models. |
| Approach: | a new contrastive learning framework is developed to improve general large language models . HMCL integrates 3-level semantic differentiation and unifies contrastive and pair classification into a strategy . |
| Outcome: | HMCL outperforms unsupervised methods and supervised fine-tuning approaches in multi-domain and multilingual benchmarks. |
Estimating Mutual Information Between Dense Word Embeddings (2020.acl-main)
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| Challenge: | Word embedding-based similarity measures are among the top-performing methods on unsupervised semantic textual similarity tasks. |
| Approach: | They propose a word embedding similarity measure that is a viable alternative to correlations and gives an excellent signal that correlates well with human judgements of similarity. |
| Outcome: | The proposed model outperforms existing state-of-the-art methods on hard NLP tasks. |
Aggregating Multiple Heuristic Signals as Supervision for Unsupervised Automated Essay Scoring (2023.acl-long)
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| Challenge: | Automated Essay Scoring (AES) aims to evaluate the quality score of input essays without human intervention. |
| Approach: | They propose an unsupervised approach to evaluate the quality of input essays . they use multiple heuristic quality signals as pseudo-groundtruths to train a neural AES model . |
| Outcome: | The proposed approach achieves state-of-the-art performance on eight prompts of ASPA dataset compared with previous unsupervised methods . |
PAUSE: Positive and Annealed Unlabeled Sentence Embedding (2021.emnlp-main)
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Lele Cao, Emil Larsson, Vilhelm von Ehrenheim, Dhiana Deva Cavalcanti Rocha, Anna Martin, Sonja Horn
| Challenge: | Sentence embedding is a set of effective and versatile techniques for converting raw text into numerical vector representations. |
| Approach: | They propose a generic and end-to-end approach to embed sentences from a partially labeled dataset using supervised methods. |
| Outcome: | The proposed approach achieves state-of-the-art results using only a small fraction of labeled sentence pairs on various benchmark tasks. |
Unsupervised Named Entity Disambiguation for Low Resource Domains (2024.emnlp-main)
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| Challenge: | Existing approaches to Named Entity Disambiguation (NED) are inefficient for domain specific tasks such as searching, question answering and information extraction. |
| Approach: | They propose a unsupervised approach leveraging the concept of Group Steiner Trees which can identify the most relevant candidate for entity disambiguation using contextual similarities across candidate entities for all the mentions present in a document. |
| Outcome: | The proposed approach outperforms the state-of-the-art methods by more than 40% in terms of Precision@1 and Hit@5 across various domain-specific datasets. |
Unifying Discrete and Continuous Representations for Unsupervised Paraphrase Generation (2023.emnlp-main)
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Mingfeng Xue, Dayiheng Liu, Wenqiang Lei, Jie Fu, Jian Lan, Mei Li, Baosong Yang, Jun Xie, Yidan Zhang, Dezhong Peng, Jiancheng Lv
| Challenge: | Existing unsupervised paraphrase generation methods require large-scale, manually annotated paraphrase datasets, which are labor-intensive to build. |
| Approach: | They propose a self-supervised pseudo-data construction method that generates diverse pseudo-paraphrases in distinct surface structures for a given sentence. |
| Outcome: | The proposed method generates diverse pseudo-paraphrases in distinct surface structures for a given sentence. |
ClusterLLM: Large Language Models as a Guide for Text Clustering (2023.emnlp-main)
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| Challenge: | Extensive experiments on 14 datasets show that ClusterLLM consistently improves clustering quality, at an average cost of $0.6 per dataset. |
| Approach: | They propose a text clustering framework that leverages feedback from an instruction-tuned large language model, such as ChatGPT. |
| Outcome: | Extensive experiments on 14 datasets show that ClusterLLM consistently improves clustering quality, at an average cost of $0.6 per dataset. |
Language Models and Semantic Relations: A Dual Relationship (2024.lrec-main)
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| Challenge: | Existing studies on language models for the extraction of semantic relations have focused on injecting semantic knowledge into these models to enhance them. |
| Approach: | They propose to extract lexical semantic relations from a BERT model and inject them into it using unsupervised methods based on semantic similarity at word and sentence levels. |
| Outcome: | The proposed method allows to enrich a BERT model without using any external semantic resource. |
MCTS: A Multi-Reference Chinese Text Simplification Dataset (2024.lrec-main)
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Ruining Chong, Luming Lu, Liner Yang, Jinran Nie, Zhenghao Liu, Shuo Wang, Shuhan Zhou, Yaoxin Li, Erhong Yang
| Challenge: | Existing studies on text simplification systems have focused on unsupervised methods due to the limited evaluation data in language and domain. |
| Approach: | They propose a Chinese text simplification dataset that provides a detailed analysis and an annotation process. |
| Outcome: | The proposed dataset evaluates the performance of unsupervised methods and advanced large language models. |
Multilingual Previously Fact-Checked Claim Retrieval (2023.emnlp-main)
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Matúš Pikuliak, Ivan Srba, Robert Moro, Timo Hromadka, Timotej Smoleň, Martin Melišek, Ivan Vykopal, Jakub Simko, Juraj Podroužek, Maria Bielikova
| Challenge: | Fact-checkers are often hampered by the sheer amount of online content that needs to be fact-checked. |
| Approach: | They propose a multilingual dataset for previously fact-checked claim retrieval using social media posts and 206k fact- checks in 39 languages written by professional fact- checkers. |
| Outcome: | The proposed method improves on the previously unsupervised method and shows that a multilingual dataset has its complexities and needs to be carefully interpreted. |
Multilingual Substitution-based Word Sense Induction (2024.lrec-main)
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| Challenge: | Word Sense Induction is the task of finding senses of an ambiguous word . many approaches to WSI are language-specific and are not easily adaptable to new languages. |
| Approach: | They propose to use multilingual substitution-based WSI methods that generalize to any language supported by the underlying multilingual language model with minimal to no adaptation required. |
| Outcome: | The proposed methods perform on par with monolingual approaches on popular English datasets while being language-specific. |
Unsupervised Hallucination Detection by Inspecting Reasoning Processes (2025.emnlp-main)
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| Challenge: | Unsupervised hallucination detection aims to identify hallucines generated by large language models without relying on labeled data. |
| Approach: | They propose an unsupervised method to detect hallucinated content by large language models . they use internal representations intrinsic to factual correctness to prompt the model to verify the truthfulness of a given statement . |
| Outcome: | The proposed framework outperforms existing unsupervised methods and is fully unsupervised and low cost. |
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. |