UNSUPERVISED METHOD

Parallel Text Alignment and Monolingual Parallel Corpus Creation from Philosophical Texts for Text Simplification (2021.naacl-srw)

Challenge:	Social media platforms are becoming an essential venue for online deliberation . stance detection is a task to determine whether a text is in favor of, against, or unrelated to a given topic.
Approach:	They propose an unsupervised method to detect the stance of argumentative claims with respect to a topic.
Outcome:	The proposed method outperforms BERT and can be comparable to other methods.

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Stefan Paun

Massively Multilingual Transfer for NER (P19-1)

Challenge:	Existing methods for text simplification require a lot of annotated data, however there are few suitable tools for this task.
Approach:	They propose an unsupervised method for aligning text based on Doc2Vec embeddings and an alignment algorithm capable of aligning texts at different levels.
Outcome:	The proposed method can be used to create a monolingual parallel corpus composed of the works of early modern philosophers and their corresponding simplified versions.

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Afshin Rahimi, Yuan Li, Trevor Cohn

An Unsupervised Method for Building Sentence Simplification Corpora in Multiple Languages (2021.findings-emnlp)

Challenge:	Existing approaches for cross-lingual transfer use a single source language, but there are exceptions.
Approach:	They propose two techniques for modulating the transfer, suitable for zero-shot or few-shot learning, respectively.
Outcome:	The proposed methods are much more effective than baseline models and rival oracle selection of the single best individual model.

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Xinyu Lu, Jipeng Qiang, Yun Li, Yunhao Yuan, Yi Zhu

Unsupervised Domain Adaptation for Keyphrase Generation using Citation Contexts (2024.findings-emnlp)

Challenge:	Existing methods to build parallel sentence simplification corpora are limited . SS is used to rephrase sentences into simpler forms for those with cognitive disabilities .
Approach:	They propose to build SS corpora from large-scale bilingual translation corpors using a parallel approach.
Outcome:	The proposed method outperforms the existing methods on WikiLarge and achieves state-of-the-art results.

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Florian Boudin, Akiko Aizawa

Unsupervised Multilingual Sentence Embeddings for Parallel Corpus Mining (2020.acl-srw)

Challenge:	Existing methods for keyphrase generation are limited to resource-rich languages.
Approach:	They propose to extract silver-standard keyphrases from citation contexts to create synthetic labeled data for domain adaptation.
Outcome:	The proposed method produces significant and consistent improvements over baselines across three domains.

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Ivana Kvapilíková, Mikel Artetxe, Gorka Labaka, Eneko Agirre, Ondřej Bojar

Estimating Semantic Similarity between In-Domain and Out-of-Domain Samples (2023.starsem-1)

Challenge:	Existing models of multilingual sentence embeddings require large parallel data resources which are not available for low-resource languages.
Approach:	They propose an unsupervised method to derive multilingual sentence embeddings using monolingual data.
Outcome:	The proposed method improves on two parallel corpus mining tasks and for other languages.

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Rhitabrat Pokharel, Ameeta Agrawal

Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection (N18-1)

Challenge:	Prior work typically defines out-of-domain (OOD) or out- of-distribution (OOdist) samples as those that originate from dataset(s) or source(s), but for the same task.
Approach:	They propose to use supervised methods to identify OOD/OODist samples without using a trained model.
Outcome:	The proposed method is able to identify OOD/OODist samples without a trained model.

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Haw-Shiuan Chang, Ziyun Wang, Luke Vilnis, Andrew McCallum

Unsupervised Bitext Mining and Translation via Self-Trained Contextual Embeddings (2020.tacl-1)

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.

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Phillip Keung, Julian Salazar, Yichao Lu, Noah A. Smith

TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning (2021.findings-emnlp)

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.

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Kexin Wang, Nils Reimers, Iryna Gurevych

Boot and Switch: Alternating Distillation for Zero-Shot Dense Retrieval (2023.findings-emnlp)

Challenge:	Existing methods to learn sentence embeddings require labeled data, but it is expensive.
Approach:	They propose an unsupervised method which learns sentence embeddings using unlabeled data . they propose a transformer-based sequence denoising auto-encoder which can be used for training .
Outcome:	The proposed method outperforms existing methods on four datasets from heterogeneous domains.

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Fan Jiang, Qiongkai Xu, Tom Drummond, Trevor Cohn

Memory and Knowledge Augmented Language Models for Inferring Salience in Long-Form Stories (2021.emnlp-main)

Challenge:	Existing approaches to enhance dense retrieval models are unwieldy, such as requiring explicit supervision, complex model architectures, or massive external models.
Approach:	They propose an unsupervised method to enhance passage retrieval in zero-shot settings by iterating a loop that a dense retriever learns from supervision signals provided by a reranker.
Outcome:	The proposed method outperforms leading supervised and unsupervised retrievers on the BEIR benchmark while showing strong adaptation abilities to tasks and domains that were unseen during training.

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David Wilmot, Frank Keller

Flip-Flop Consistency: Unsupervised Training for Robustness to Prompt Perturbations in LLMs (2026.acl-long)

Challenge:	Detecting salient events is an essential part of understanding narrative, and is used to aid storyline writing and summarisation.
Approach:	They propose an unsupervised method for salience detection derived from Barthes Cardinal Functions and theories of surprise and apply it to longer narrative forms.
Outcome:	The proposed method improves performance over a non-knowledgebase and memory augmented language model on longer works.

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Parsa Hejabi, Elnaz Rahmati, Alireza Salkhordeh Ziabari, Morteza Dehghani

On the Transformation of Latent Space in Fine-Tuned NLP Models (2022.emnlp-main)

Challenge:	Large Language Models (LLMs) often produce inconsistent answers when faced with different phrasings of the same prompt.
Approach:	They propose an unsupervised method that flips the phrasings of prompts into a hard pseudo-label . they use Consensus Cross-Entropy to create a consensus, and representation alignment loss to pull lower-confidence predictors toward consensus .
Outcome:	The proposed method raises observed agreement by 11.62% and improves mean F1 by 8.94% on 11 datasets spanning four NLP tasks .

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Nadir Durrani, Hassan Sajjad, Fahim Dalvi, Firoj Alam

Language Models

Challenge:	a large body of work analyzed the knowledge learned within representations of pre-trained models.
Approach:	They use hierarchical clustering to discover latent concepts in representational space . they compare pre-trained and fine-tuned models and perform a thorough analysis .
Outcome:	The results show that the model space evolves towards task-specific concepts whereas the lower layers retain generic concepts acquired in the pre-trained model.

Compact Language Models with Iterative Text Refinement for Health Dialogue Summarization (2026.eacl-long)

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Kellen Tan Cheng, Ganesh Ramesh, Nafiul Rashid, Geoffrey Jay Tso, Jilong Kuang

Dialogue Systems & Conversational Agents

Challenge:	Health wellness agents typically rely on large language models (LLMs) for response generation, where contextual information from prior conversations can be utilized for response grounding and personalization.
Approach:	They propose to use large language models to generate high-quality health dialogue summaries by using iterative feedback.
Outcome:	The proposed method outperforms baseline on open-source and proprietary benchmarks and can run on local compute without a GPU.

Unsupervised Question Answering via Answer Diversifying (2022.coling-1)

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Yuxiang Nie, Heyan Huang, Zewen Chi, Xian-Ling Mao

Establishing degrees of closeness between audio recordings along different dimensions using large-scale cross-lingual models (2024.findings-eacl)

Challenge:	Existing extractive question answering methods use labeled data to train QA models.
Approach:	They propose an unsupervised method by diversifying answers by using data construction, data augmentation and denoising filter.
Outcome:	The proposed method outperforms previous models on five benchmark datasets . it shows strong performance in the few-shot learning setting .

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Maxime Fily, Guillaume Wisniewski, Severine Guillaume, Gilles Adda, Alexis Michaud

Speech & Audio in NLP

Challenge:	Existing methods to analyze speech representations using pretraining data are difficult to achieve for endangered languages.
Approach:	They propose an unsupervised method to examine the level of abstraction in vector representations of speech from a pretrained model to determine their level of abstractness.
Outcome:	The proposed method is fully unsupervised and could be used in comparative studies on under-documented languages.

A Query-Driven Topic Model (2021.findings-acl)

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Zheng Fang, Yulan He, Rob Procter

Try to Substitute: An Unsupervised Chinese Word Sense Disambiguation Method Based on HowNet (2020.coling-main)

Challenge:	Topic modeling is an unsupervised method for revealing the hidden semantic structure of a corpus.
Approach:	They propose a query-driven topic model that allows users to specify a simple query in words or phrases and return query-related topics.
Outcome:	The proposed model is particularly attractive when the query has a low occurrence in a text corpus, making it difficult for traditional topic models to identify relevant topics.

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Bairu Hou, Fanchao Qi, Yuan Zang, Xurui Zhang, Zhiyuan Liu, Maosong Sun

TopWORDS-Poetry: Simultaneous Text Segmentation and Word Discovery for Classical Chinese Poetry via Bayesian Inference (2023.emnlp-main)

Challenge:	Existing unsupervised methods for word sense disambiguation cannot work for HowNet-based WSD because of its uniqueness.
Approach:	They propose a method which exploits the masked language model task of pre-trained language models to conduct word sense disambiguation using a lexical knowledge base as the sense inventory.
Outcome:	The proposed method achieves significantly better performance than baseline methods.

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Changzai Pan, Feiyue Li, Ke Deng

Syntactic Text Processing

Challenge:	Experimental studies confirm that TopWORDS-Poetry can successfully segment poetry words without pre-given vocabulary or training corpus.
Approach:	They propose an unsupervised method that can achieve reliable text segmentation and word discovery for classical Chinese poetry simultaneously without pre-given vocabulary or training corpus.
Outcome:	Experimental results show that TopWORDS-Poetry can segment poetry lines into meaningful words with high quality without pre-given vocabulary or training corpus.

Bridging the Gap Between BabelNet and HowNet: Unsupervised Sense Alignment and Sememe Prediction (2023.eacl-main)

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Xiang Zhang, Ning Shi, Bradley Hauer, Grzegorz Kondrak

Multimodality

Challenge:	Sememes are the minimum semantic units of natural languages, but their use is limited by a lack of available sememe knowledge bases.
Approach:	They propose to use sense alignment to connect BabelNet with HowNet by relaxing constraints until a complete alignment is achieved.
Outcome:	The proposed method improves on previous supervised methods by 12% . it is based on interpretable propagation of sememe information between lexical resources .

SumCSE: Summary as a transformation for Contrastive Learning (2024.findings-naacl)

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Raghuveer Thirukovalluru, Xiaolan Wang, Jun Chen, Shuyang Li, Jie Lei, Rong Jin, Bhuwan Dhingra

Controllable Natural Language Generation with Contrastive Prefixes (2022.findings-acl)

Challenge:	Sentence embedding models are typically trained using contrastive learning (CL) using human annotations directly or by repurposing other annotated datasets.
Approach:	They propose to use generative language models to generate CL data using annotated data.
Outcome:	The proposed method outperforms the previous best unsupervised method by 1.8 points and SimCSE, a strong supervised baseline by 0.3 points on the semantic text similarity (STS) benchmark.

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Jing Qian, Li Dong, Yelong Shen, Furu Wei, Weizhu Chen

Language Models

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.

MyFixit: An Annotated Dataset, Annotation Tool, and Baseline Methods for Information Extraction from Repair Manuals (2020.lrec-1)

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Nima Nabizadeh, Dorothea Kolossa, Martin Heckmann

Unsupervised Graph-Text Mutual Conversion with a Unified Pretrained Language Model (2023.acl-long)

Challenge:	Textual instructions are among the most widely used media for learning and teaching . however, to create autonomous systems, it is difficult to extract task knowledge from text .
Approach:	They propose methods that can extract information from repair manuals from a semi-structured dataset . they propose a bag-of-n-grams similarity method and deep-learning-based sequence labeling model .
Outcome:	The proposed methods can extract the needed tools and disassembled parts from repair manuals.

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Yi Xu, Shuqian Sheng, Jiexing Qi, Luoyi Fu, Zhouhan Lin, Xinbing Wang, Chenghu Zhou

Embarrassingly Simple Unsupervised Aspect Extraction (2020.acl-main)

Challenge:	Existing unsupervised approaches for learning knowledge graphs require multiple modules and require entity information or relation type for training.
Approach:	They propose a method that uses a unified pretrained language model to achieve fully unsupervised graph-text mutual conversion for the first time.
Outcome:	The proposed method outperforms state-of-the-art methods for G2T and T2G tasks by fine-tuning only one pretrained model.

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Stéphan Tulkens, Andreas van Cranenburgh

Sentiment Analysis

Challenge:	Existing systems for aspect extraction are supervised, but are unlikely to transfer well between domains.
Approach:	They propose a novel approach that uses an RBF kernel to generate a single-head attention mechanism for aspect extraction from text.
Outcome:	The proposed method is based on an RBF kernel and can be applied to new domains and languages.

Fixing Translation Divergences in Parallel Corpora for Neural MT (D18-1)

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MinhQuang Pham, Josep Crego, Jean Senellart, François Yvon

Multilinguality

Challenge:	Existing methods to detect translation divergences from parallel corpora are noisy and limited in size.
Approach:	They propose an unsupervised method for detecting translation divergences in parallel sentences . they use a neural network that computes cross-lingual sentence similarity scores .
Outcome:	The proposed method improves translation performance for English-French and English-German translation tasks.

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Moritz Plenz, Juri Opitz, Philipp Heinisch, Philipp Cimiano, Anette Frank

Multimodality

Challenge:	Arguments often do not make explicit how a conclusion follows from its premises . we present a method for constructing Contextualized Commonsense Knowledge Graphs (CCKGs) that is efficient and high-quality .
Approach:	They propose an unsupervised method for constructing Contextualized Commonsense Knowledge Graphs (CCKGs) they use triplet similarities to extract contextually relevant knowledge paths .
Outcome:	The proposed method outperforms baselines and a GPT-3 based system in a knowledge-intense argumentation task.

ESimCSE: Enhanced Sample Building Method for Contrastive Learning of Unsupervised Sentence Embedding (2022.coling-1)

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Xing Wu, Chaochen Gao, Liangjun Zang, Jizhong Han, Zhongyuan Wang, Songlin Hu

Wider & Closer: Mixture of Short-channel Distillers for Zero-shot Cross-lingual Named Entity Recognition (2022.emnlp-main)

Challenge:	a new method for learning unsupervised sentence embeddings is proposed . unsup-SimCSE is biased because of the length information encoded into the sentence embeds .
Approach:	They propose a new unsupervised sentence embedding method that uses dropout to obtain positive pairs from a pre-trained Transformer encoder.
Outcome:	The proposed method outperforms the state-of-the-art unsup-SimCSE on a STS task.

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Jun-Yu Ma, Beiduo Chen, Jia-Chen Gu, Zhenhua Ling, Wu Guo, Quan Liu, Zhigang Chen, Cong Liu

Unsupervised Lexical Substitution with Decontextualised Embeddings (2022.coling-1)

Challenge:	Existing mainstream methods for zero-shot cross-lingual named entity recognition ignore the rich and complementary information lying in the intermediate layers of pre-trained language models and domain-invariant information is easily lost during transfer.
Approach:	They propose a mixture of short-channel distillers to fully interact the rich hierarchical information in the teacher model and to transfer knowledge to the student model sufficiently and efficiently.
Outcome:	The proposed method shows great generalization and compatibility across languages and fields.

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Takashi Wada, Timothy Baldwin, Yuji Matsumoto, Jey Han Lau

Discovering Low-rank Subspaces for Language-agnostic Multilingual Representations (2022.emnlp-main)

Challenge:	Existing methods for lexical substitution using pre-trained language models have some limitations.
Approach:	They propose an unsupervised method for lexical substitution using pre-trained language models.
Outcome:	The proposed method outperforms baseline models and establishes a state-of-the-art without supervision or fine-tuning.

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Zhihui Xie, Handong Zhao, Tong Yu, Shuai Li

How Large Are Lions? Inducing Distributions over Quantitative Attributes (P19-1)

Challenge:	Existing studies show that pre-trained ML-LMs can achieve zero-shot cross-lingual transfer without explicit cross-linguistic supervision.
Approach:	They propose a method to remove language-specific factors from multilingual embedding spaces by using a single value decomposition method with multiple monolingual corpora as input.
Outcome:	The proposed method can boost language agnosticism without finetuning . Empirical results show that it consistently leads to improvements over existing models.

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Yanai Elazar, Abhijit Mahabal, Deepak Ramachandran, Tania Bedrax-Weiss, Dan Roth

Challenge:	Current NLP systems have little knowledge about quantitative attributes of objects and events.
Approach:	They propose to use web data to create a resource consisting of distributions over physical quantities associated with objects, adjectives, and verbs.
Outcome:	The proposed method compares favorably with state-of-the-art results on existing datasets for relative comparisons of nouns and adjectives and on a new dataset.

BottleSum: Unsupervised and Self-supervised Sentence Summarization using the Information Bottleneck Principle (D19-1)

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Peter West, Ari Holtzman, Jan Buys, Yejin Choi

SimplifyUR: Unsupervised Lexical Text Simplification for Urdu (2020.lrec-1)

Challenge:	Existing approaches to extractive and abstractive summarization rely on large-scale parallel corpora of input text and output summaries for direct supervision.
Approach:	They propose an unsupervised approach to sentence summarization using the Information Bottleneck principle.
Outcome:	The proposed method outperforms unsupervised models on automatic metrics and human evaluation along multiple attributes.

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Namoos Hayat Qasmi, Haris Bin Zia, Awais Athar, Agha Ali Raza

MOVER: Mask, Over-generate and Rank for Hyperbole Generation (2022.naacl-main)

Challenge:	Existing methods for text simplification for Urdu rely on manual lexicons and simplified corpora, but are not applicable to the language.
Approach:	They propose an unsupervised method for automatic text simplification for Urdu using word embeddings and morphological features.
Outcome:	The proposed method achieves BLEU score of 80.15 and SARI score of 42.02 on simple text generated on simplified corpora and human evaluations for correctness, grammaticality, meaning-preservation and simplicity.

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Yunxiang Zhang, Xiaojun Wan

Balancing Objectives in Counseling Conversations: Advancing Forwards or Looking Backwards (2020.acl-main)

Challenge:	despite being a common figure of speech, hyperbole is under-researched in Figurative Language Processing . we use an unsupervised method to generate hyperbolic paraphrases from literal sentences .
Approach:	They propose an unsupervised method for hyperbole generation that does not require parallel literal-hyperbole pairs.
Outcome:	The proposed method outperforms baseline systems and is based on a large-scale English hyperbole corpus.

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Justine Zhang, Cristian Danescu-Niculescu-Mizil

Dialogue Systems & Conversational Agents

Challenge:	Figure 1: a counseling conversation in which participants make choices that can orient the flow of the interaction.
Approach:	They propose an unsupervised method to quantify how counselors manage this balance by mapping each utterance to an orientation axis that captures the degree to which it is intended to direct the flow of the conversation forwards or backwards.
Outcome:	The proposed method allows to characterize counselor behaviors in a large dataset of crisis counseling conversations.

A STEP towards Interpretable Multi-Hop Reasoning:Bridge Phrase Identification and Query Expansion (2022.lrec-1)

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Fan Luo, Mihai Surdeanu

Question Answering

Challenge:	Identifying bridge phrases remains one of the challenges for multi-hop question answering .
Approach:	They propose an unsupervised method for the identification of bridge phrases in multi-hop question answering . they construct a graph of noun phrases from the question and available context .
Outcome:	The proposed method improves all downstream components in a multi-hop QA system.

Simple Unsupervised Summarization by Contextual Matching (P19-1)

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Jiawei Zhou, Alexander Rush

Learning Dynamic Contextualised Word Embeddings via Template-based Temporal Adaptation (2023.acl-long)

Challenge:	Existing methods for sentence summarization require a large amount of parallel data for supervision to work.
Approach:	They propose an unsupervised method for sentence summarization using only language modeling.
Outcome:	The proposed method maintains continuous contextual matching while maintaining output fluency without any paired examples.

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Xiaohang Tang, Yi Zhou, Danushka Bollegala

Swap and Predict – Predicting the Semantic Changes in Words across Corpora by Context Swapping (2023.findings-emnlp)

Challenge:	Existing methods for learning dynamic contextualised word embeddings do not capture temporal semantic variations of words.
Approach:	They propose a method for learning DCWEs by time-adapting a pretrained Masked Language Model using time-sensitive templates.
Outcome:	The proposed method significantly reduces the perplexity of test sentences in C2 outperforming the current state-of-the-art.

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Taichi Aida, Danushka Bollegala

Evaluating Language Translation Models by Playing Telephone (2025.emnlp-main)

Challenge:	Detecting semantic changes of words is an important task for various NLP applications that must make time-sensitive predictions.
Approach:	They propose a method that randomly swaps contexts between two different corpora to detect whether a given word changes its meaning . they then use a pretrained masked language model to generate contextualised word embeddings of w, which are then used to predict the semantic changes of words in four languages .
Outcome:	The proposed method achieves significant performance improvements compared to baselines for the English semantic change prediction task.

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Syeda Jannatus Saba, Steven Skiena

Machine Translation

Challenge:	Existing language models are inadequate for evaluating machine translation systems . current evaluation methods are costly and require specialized expertise to prepare and score gold standard translations .
Approach:	They propose an unsupervised method to generate training data for translation evaluation by repeated rounds of translation between source and target languages.
Outcome:	The proposed method outperforms a popular translation evaluation system on two tasks . human annotation is costly and requires specialized expertise to prepare and score gold standard translations .

Neural-DINF: A Neural Network based Framework for Measuring Document Influence (2020.acl-main)

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Jie Tan, Changlin Yang, Ying Li, Siliang Tang, Chen Huang, Yueting Zhuang

Challenge:	Existing methods to measure scholarly impact of documents without citations only consider word frequency change.
Approach:	They propose a neural network framework that measures document influence without citations by using word frequency changes and word semantic shifts.
Outcome:	The proposed model outperforms existing models on document influence evaluation without citations.

Word2Sense: Sparse Interpretable Word Embeddings (P19-1)

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Abhishek Panigrahi, Harsha Vardhan Simhadri, Chiranjib Bhattacharyya

Improving Unsupervised Keyphrase Extraction using Background Knowledge (L18-1)

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.

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Yang Yu, Vincent Ng

Towards Speaker Verification for Crowdsourced Speech Collections (2022.lrec-1)

Challenge:	Existing methods of keyphrase extraction are supervised and unsupervised . Topical PageRank uses topical information to extract the top topics of a document .
Approach:	They propose an unsupervised method for keyphrase extraction based on Wikipedia . they construct a semantic graph and transform the extraction problem into an optimization problem .
Outcome:	The proposed method improves over other state-of-the-art models by more than 2% in F1-score.

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John Mendonca, Rui Correia, Mariana Lourenço, João Freitas, Isabel Trancoso

Multimodality

Challenge:	Existing methods to detect low quality work do not address the correctness of the data.
Approach:	They propose an unsupervised method for measuring speaker metadata plausibility of a collection, i.e., evaluating the match (or lack thereof) between contributors and speakers.
Outcome:	The proposed method shows high precision in automatically classifying contributor alignment (>0.94).

AttenWalker: Unsupervised Long-Document Question Answering via Attention-based Graph Walking (2023.findings-acl)

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Yuxiang Nie, Heyan Huang, Wei Wei, Xian-Ling Mao

In the LLM era, Word Sense Induction remains unsolved (2025.findings-acl)

Challenge:	Existing methods for annotating long-document question answering are based on short documents and can hardly incorporate long-range information.
Approach:	They propose an unsupervised method to generate long-document question answering pairs . they propose a method to aggregate and generate answers with long-range dependency .
Outcome:	The proposed method outperforms existing methods on NarrativeQA and Qasper.

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Anna Mosolova, Marie Candito, Carlos Ramisch

Text Clustering

Challenge:	Existing word sense induction methods rely on predefined sense inventories of arbitrary granularity and require labor intensive sense-annotated data.
Approach:	They propose to evaluate an LLM-based word sense induction method for English . they use data augmentation sources, corpus and lexicon, and semi-supervised scenarios to augment the data.
Outcome:	The proposed method surpasses the previous SOTA system on a English set by 3.3%.

Segment First or Comprehend First? Explore the Limit of Unsupervised Word Segmentation with Large Language Models (2025.acl-long)

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Zihong Zhang, Liqi He, Zuchao Li, Lefei Zhang, Hai Zhao, Bo Du

Multilingual Previously Fact-Checked Claim Retrieval (2023.emnlp-main)

Challenge:	Existing approaches to measure word segmentation only assess the language model's understanding of the overall meaning of sentences, lacking an evaluation of the language models' understanding capabilities at a fine-grained level.
Approach:	They propose a framework to explore the limit of unsupervised word segmentation with Large Language Models (LLMs) they employ current mainstream LLMs to perform word segmentations across multiple languages .
Outcome:	The proposed method improves on existing methods and combines the advanced pattern recognition capabilities of Aho-Corasick automata with the deep insights of well-pretrained LLMs.

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Matúš Pikuliak, Ivan Srba, Robert Moro, Timo Hromadka, Timotej Smoleň, Martin Melišek, Ivan Vykopal, Jakub Simko, Juraj Podroužek, Maria Bielikova

STEntConv: Predicting Disagreement between Reddit Users with Stance Detection and a Signed Graph Convolutional Network (2024.lrec-main)

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.

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Isabelle Lorge, Li Zhang, Xiaowen Dong, Janet Pierrehumbert

Sentiment Analysis

Challenge:	Existing methods to detect disagreements on social media platforms have focused on supplementing textual information with user network information, such as Twitter's following system, retweets and hashtags.
Approach:	They propose a method which builds a graph of users and named entities and trains a Signed Graph Convolutional Network to detect disagreement between comment and reply posts.
Outcome:	The proposed model builds a graph of users and named entities weighted by stance and trains a Signed Graph Convolutional Network (SGCN) to detect disagreement between comment and reply posts.

Token-length Bias in Minimal-pair Paradigm Datasets (2024.lrec-main)

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Naoya Ueda, Masato Mita, Teruaki Oka, Mamoru Komachi

SemCSE: Semantic Contrastive Sentence Embeddings Using LLM-Generated Summaries For Scientific Abstracts (2025.emnlp-main)

Challenge:	Minimal-pair paradigm datasets are used to evaluate the linguistic knowledge of language models and provide an unsupervised method of acceptability judgment.
Approach:	They propose a debiased minimal pair generation method that allows MPP datasets to evaluate the linguistic knowledge of a language model correctly.
Outcome:	The proposed method is based on the percentage of minimal pairs in the MPP dataset where the model assigns a higher sentence log-likelihood than an unacceptable sentence.

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Marc Felix Brinner, Sina Zarrieß