CLASSIFIERS

Adversarial Removal of Demographic Attributes from Text Data (D18-1)

Challenge:	Anaphora resolution systems designed for CONLL 2012 dataset can handle key aspects of the full anaphora task such as the identification of singletons and of certain types of non-referring expressions.
Approach:	They propose an architecture to identify non-referring expressions and build coreference chains, including singletons, using system mentions.
Outcome:	The proposed model performs better on the CONLL 2012 dataset than the state-of-the-art system.

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Yanai Elazar, Yoav Goldberg

How Did This Get Funded?! Automatically Identifying Quirky Scientific Achievements (2021.acl-long)

Challenge:	Recent advances in Representation Learning and Adversarial Training remove unwanted features from the learned representation.
Approach:	They show that demographic information of authors is encoded in the intermediate representations learned by text-based neural classifiers.
Outcome:	The proposed approach achieves higher accuracies on the same dataset, the authors show . they show that the proposed approach is effective in removing unwanted features from the learned representations.

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Chen Shani, Nadav Borenstein, Dafna Shahaf

Reasoning

Challenge:	Humor is an important social phenomenon, serving complex social and psychological functions.
Approach:	They propose a novel algorithm for automatically detecting funny scientific papers . they use a dataset containing thousands of funny papers to learn classifiers .
Outcome:	The proposed task is based on a dataset containing thousands of funny scientific papers . it is a novel task that can be automated and improves on existing methods .

Stability of Syntactic Dialect Classification over Space and Time (2022.coling-1)

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Jonathan Dunn, Sidney Wong

NoEl: An Annotated Corpus for Noun Ellipsis in English (2020.lrec-1)

Challenge:	a paper examines the degree to which dialect classifiers remain stable over time . it finds that the models remain robust over time with a fixed decay rate .
Approach:	They construct a test set for 12 dialects of English that spans three years at monthly intervals with a fixed spatial distribution across 1,120 cities.
Outcome:	The proposed model can reveal linguistic variation over space and time.

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Payal Khullar, Kushal Majmundar, Manish Shrivastava

Complaint Analysis and Classification for Economic and Food Safety (D19-51)

Challenge:	Ellipsis resolution is an important step to improve the accuracy of mainstream natural language processing tasks such as information retrieval, event extraction, dialog systems, etc.
Approach:	They extend the study of ellipsis by annotating a corpus for noun ellippsis and closely related phenomenon using the first hundred movies of Cornell Movie Dialogs Dataset.
Outcome:	The proposed corpus has 946 instances of exophoric and endophorical noun ellipsis, making it the biggest resource of nouns in English, to the best of our knowledge.

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João Filgueiras, Luís Barbosa, Gil Rocha, Henrique Lopes Cardoso, Luís Paulo Reis, João Pedro Machado, Ana Maria Oliveira

The Authors Matter: Understanding and Mitigating Implicit Bias in Deep Text Classification (2021.findings-acl)

Challenge:	Governmental institutions are using artificial intelligence to deal with specific problems and exploit their huge amounts of structured and unstructured information.
Approach:	They propose to use natural language processing and machine learning to classify complaints . they use feature-based approaches and traditional classifiers to analyze complaints based on citizen feedback .
Outcome:	The proposed methods have accuracy scores above 70% and can be used to improve public services.

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Haochen Liu, Wei Jin, Hamid Karimi, Zitao Liu, Jiliang Tang

DeezyMatch: A Flexible Deep Learning Approach to Fuzzy String Matching (2020.emnlp-demos)

Challenge:	Existing studies on text classification have focused on the bias towards the individuals mentioned in the text content.
Approach:	They propose a framework to mitigate implicit bias in text classification models based on demographic attributes of authors . they propose to use this framework to train deep text classifiers to make predictions on the right features .
Outcome:	The proposed framework outperforms existing models significantly in fairness and performance.

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Kasra Hosseini, Federico Nanni, Mariona Coll Ardanuy

Discussion Tracker: Supporting Teacher Learning about Students’ Collaborative Argumentation in High School Classrooms (2020.coling-demos)

Challenge:	DeezyMatch is a free, open-source software library written in Python for fuzzy string matching and candidate ranking.
Approach:	They propose to use DeezyMatch to train new classifiers and fine-tune a pretrained model to generate rich vector representations from string inputs.
Outcome:	The proposed algorithm can be used to find the best matching candidates in large knowledge bases and query sets.

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Luca Lugini, Christopher Olshefski, Ravneet Singh, Diane Litman, Amanda Godley

Challenge:	Discussion Tracker provides teachers with data about argument moves, specificity and collaboration .
Approach:	They have developed a classroom discussion analytics system that leverages natural language processing to classify argument moves, specificity and collaboration.
Outcome:	The proposed system performs with moderate to substantial agreement with humans in a classroom setting.

Small-Text: Active Learning for Text Classification in Python (2023.eacl-demo)

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Christopher Schröder, Lydia Müller, Andreas Niekler, Martin Potthast

Acoustic-Prosodic and Lexical Cues to Deception and Trust: Deciphering How People Detect Lies (2020.tacl-1)

Challenge:	small-text is an easy-to-use active learning library for text classification . it features a variety of pre-implemented state-of-the-art query strategies and stopping criteria .
Approach:	They introduce small-text, an easy-to-use active learning library for Python . it offers pool-based active learning for single- and multi-label text classification . they find it matches vanilla transformer fine-tuning in terms of classification accuracy .
Outcome:	The proposed library outperforms vanilla transformer fine-tuning in classification accuracy and area under the curve.

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Xi (Leslie) Chen, Sarah Ita Levitan, Michelle Levine, Marko Mandic, Julia Hirschberg

Challenge:	LieCatcher collects ratings of perceived deception using corpus of deceptive and truthful interviews . acoustic-prosodic and linguistic characteristics of language trusted and mistrusted are not reliable cues .
Approach:	They used a game framework to collect ratings of perceived deception using deceptive and truthful interviews to understand how perception aligns with reality.
Outcome:	The proposed framework detects deception using a corpus of deceptive and truthful interviews.

Assessing Social License to Operate from the Public Discourse on Social Media (2020.coling-industry)

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Chang Xu, Cecile Paris, Ross Sparks, Surya Nepal, Keith VanderLinden

Challenge:	Social License to Operate (SLO) is the level of support organisations gain from the public.
Approach:	They propose to extract and transform peoples’ stances towards an organisation into SLO levels by performing a chain of three text classification tasks.
Outcome:	The proposed system extracts and transforms peoples’ stances towards an organisation into SLO levels.

Challenges and Remedies of Domain-Specific Classifiers as LLM Guardrails: Self-Harm as a Case Study (2025.naacl-industry)

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Bing Zhang, Guang-Jie Ren

Investigating Web Corpus Filtering Methods for Language Model Development in Japanese (2024.naacl-srw)

Challenge:	Large Language Models (LLMs) have impressive capabilities in generating human-like text, but they pose significant risks in many domains and require guardrails throughout the lifecycle.
Approach:	They propose to use a self-harm detector to test the performance of LLM guardrails in real-world environments.
Outcome:	The proposed model performs poorly in open and closed domains and is almost unusable in the real world.

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Rintaro Enomoto, Arseny Tolmachev, Takuro Niitsuma, Shuhei Kurita, Daisuke Kawahara

Detecting Primary Progressive Aphasia (PPA) from Text: A Benchmarking Study (2026.findings-eacl)

Challenge:	a high quality web corpus is essential for large language models to be developed . strong filtering methods can lead to lesser performance in downstream tasks .
Approach:	They build classifiers and language models that can process large amounts of corpora quickly enough for pretraining LLMs.
Outcome:	The proposed method is the most accurate and leads to lesser performance in downstream tasks.

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Ghofrane Merhbene, Fabian Lecron, Philippe Fortemps, Bradford C. Dickerson, Mascha Kurpicz-Briki, Neguine Rezaii

Watching the AI Watchdogs: A Fairness and Robustness Analysis of AI Safety Moderation Classifiers (2025.naacl-short)

Challenge:	Primary progressive aphasia (PPA) is a neurodegenerative disorder characterized by progressive language deficits as the primary symptom.
Approach:	They benchmarked the performance of traditional machine learning models with various feature extraction techniques, transformer-based models, and large language models (LLMs) they found that transformer-Based models exceeded chance-level performance in terms of balanced accuracy, while MLP using MentalBert’s embeddings achieved the highest accuracy.
Outcome:	The proposed models outperform chance-level models in terms of balanced accuracy while using MentalBert’s embeddings achieve the highest accuracy.

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Akshit Achara, Anshuman Chhabra

Multi-Dimensional Gender Bias Classification (2020.emnlp-main)

Challenge:	ASM classifiers are designed to moderate content on social media platforms and serve as guardrails that prevent Large Language Models (LLMs) from being fine-tuned on unsafe inputs.
Approach:	They examine the fairness and robustness of four widely-used, closed-source ASM classifiers: OpenAI Moderation API, Perspective API, Google Cloud Natural Language (GCNL) API, and Clarifai API.
Outcome:	The classifiers do not unfairly classify content belonging to minority groups as unsafe compared to those belonging to majority groups and their behavior remains robust and consistent across similar inputs.

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Emily Dinan, Angela Fan, Ledell Wu, Jason Weston, Douwe Kiela, Adina Williams

Compound or Term Features? Analyzing Salience in Predicting the Difficulty of German Noun Compounds across Domains (2021.starsem-1)

Challenge:	a novel framework decomposes gender bias in text along several pragmatic and semantic dimensions . language is a primary means by which people communicate, express identities and categorize themselves . unwanted gender biases can affect downstream applications, leading to poor user experiences .
Approach:	They propose a framework that decomposes gender bias in text along several dimensions . they annotate eight large scale datasets with gender information and collect a benchmark .
Outcome:	The proposed framework decomposes gender bias in text along several pragmatic and semantic dimensions.

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Anna Hätty, Julia Bettinger, Michael Dorna, Jonas Kuhn, Sabine Schulte im Walde

Challenge:	Using domain-specific vocabulary, it is important to analyse domain-related characteristics to improve the communication between lay people and experts.
Approach:	They focus on the interaction of compound-based lexical features (such as frequency and productivity) and terminology-based features (contrasting domain-specific and general language) across word representations and classifiers.
Outcome:	The proposed model shows that the interaction of compound-based lexical features and terminology-based features across word representations and classifiers is important for a broad binary distinction into ‘easy’ vs. ‘difficult’ general-language compound frequency is sufficient, but for . a more fine-grained four-class distinction it is crucial to include contrastive termhood features and compound and constituent features.

FIND: Human-in-the-Loop Debugging Deep Text Classifiers (2020.emnlp-main)

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Piyawat Lertvittayakumjorn, Lucia Specia, Francesca Toni

What A Sunny Day ☔: Toward Emoji-Sensitive Irony Detection (D19-55)

Challenge:	Existing models are limited in the number of available datasets and lack the necessary tools to improve them.
Approach:	They propose a framework which enables humans to debug deep learning text classifiers by disabling irrelevant hidden features.
Outcome:	Experiments show that using FIND, humans can improve CNN text classifiers trained on different types of imperfect datasets.

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Shirley Anugrah Hayati, Aditi Chaudhary, Naoki Otani, Alan W Black

Variable Typing: Assigning Meaning to Variables in Mathematical Text (N18-1)

Challenge:	Existing datasets for irony detection only contain 10% of ironic tweets with emojis . 45% of internet users in the united states use an e-moji in social media .
Approach:	They propose to use emojis to analyze irony detection datasets to train classifiers.
Outcome:	The proposed pipeline can be used to analyze irony detection datasets using emojis.

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Yiannos Stathopoulos, Simon Baker, Marek Rei, Simone Teufel

Numerical Reasoning

Challenge:	Scientific documents rely on mathematics to communicate ideas and results . textual contexts are strong domainspecific conventions governing how content is presented .
Approach:	They introduce a task of assigning one mathematical type to each variable in a sentence . they also introduce 'variable typing' task that focuses on assignment of meaning to variables .
Outcome:	The proposed model is the best performing model on an extrinsic task, the authors show . their model is compared to a formula index only containing raw symbols .

Zero-shot Learning of Classifiers from Natural Language Quantification (P18-1)

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Shashank Srivastava, Igor Labutov, Tom Mitchell

Towards Agile Text Classifiers for Everyone (2023.findings-emnlp)

Challenge:	Existing methods to learn concepts from natural language are limited or no labeled examples.
Approach:	They propose a framework through which a set of explanations of a concept can be used to learn a classifier without access to any labeled examples.
Outcome:	The proposed framework outperforms previous approaches for learning with limited data and is comparable with fully supervised classifiers trained from a small number of labeled examples.

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Maximilian Mozes, Jessica Hoffmann, Katrin Tomanek, Muhamed Kouate, Nithum Thain, Ann Yuan, Tolga Bolukbasi, Lucas Dixon

A Seed Corpus of Hindu Temples in India (2020.lrec-1)

Challenge:	Text-based safety classifiers are widely used for content moderation and increasingly to tune generative language model behavior.
Approach:	They propose to use small, targeted datasets to train safety classifiers using small, iterative datasets that can be quickly developed for a particular policy.
Outcome:	The proposed method can be quickly developed for a specific policy with a labeled dataset of as few as 80 examples.

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Priya Radhakrishnan

Dynamically Refined Regularization for Improving Cross-corpora Hate Speech Detection (2022.findings-acl)

Challenge:	a scientific study of temples can reveal valuable insights into culture and heritage of India.
Approach:	They propose a platform that creates temple corpus from web text on temples.
Outcome:	The proposed platform improves the curation of temple corpus using classifiers trained on Wikipedia articles on Hindu temples.

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Tulika Bose, Nikolaos Aletras, Irina Illina, Dominique Fohr

Text Classification with Negative Supervision (2020.acl-main)

Challenge:	Hate speech classifiers exhibit performance degradation when evaluated on datasets different from the source.
Approach:	They propose to automatically identify and reduce spurious correlations using attribution methods with dynamic refinement of the list of terms that need to be regularized during training.
Outcome:	The proposed method improves performance across corpora and on different datasets.

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Sora Ohashi, Junya Takayama, Tomoyuki Kajiwara, Chenhui Chu, Yuki Arase

A Study of Nationality Bias in Names and Perplexity using Off-the-Shelf Affect-related Tweet Classifiers (2024.emnlp-main)

Challenge:	Existing models for text representations have shown state-of-the-art performance on text classification tasks, however, the discrepancy between semantic similarity of texts and labelling standards affects classifiers.
Approach:	They propose a simple multitask learning model that uses negative supervision to generate distinct representations for texts with different labels.
Outcome:	The proposed model outperforms state-of-the-art models on classification tasks in three different languages.

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Valentin Barriere, Sebastian Cifuentes

Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference (2022.tacl-1)

Challenge:	Recent research shows that named entities influence PLMs in many applications.
Approach:	They propose a method to quantify biases associated with named entities from various countries using Twitter data instead of templates or specific datasets.
Outcome:	The proposed method shows positive biases related to the language spoken in a country across all classifiers.

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Bangzheng Li, Wenpeng Yin, Muhao Chen

“#DisabledOnIndianTwitter” : A Dataset towards Understanding the Expression of People with Disabilities on Indian Twitter (2022.findings-aacl)

Challenge:	Existing methods for ultra-fine entity typing fail to capture type semantics because of the large number of types and the scarcity of data per type.
Approach:	They propose a method that formulates entity typing as a natural language inference problem . they use indirect supervision from NLI to infer type information as textual hypotheses .
Outcome:	The proposed method achieves state-of-the-art performance on the ultra-fine entity typing task with limited training data.

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Ishani Mondal, Sukhnidh Kaur, Kalika Bali, Aditya Vashistha, Manohar Swaminathan

Felipe Paula, Rodrigo Wilkens, Marco Idiart, Aline Villavicencio

Challenge:	a majority of disabled Indians exist at the margins of society with little to no access to social media . as access to ICTs and high-speed internet grows, Indian Twitter's user base is expanding to include disability influencers, activists, and everyday disabled users.
Approach:	They propose a hierarchical annotation taxonomy to classify tweets into various themes including discrimination, advocacy, and self-identification.
Outcome:	The proposed taxonomy classifies 2,384 tweets into various themes including discrimination, advocacy, and self-identification.

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FedPAGR: Federated Prototype Alignment via Geometric Refinement for Heterogeneous Architectures (2026.acl-srw)

Challenge:	Semantic Verbal Fluency tests have been used in the diagnosis of certain clinical conditions, like Dementia.
Approach:	They investigate three similarity measures for automatically identifying switches in semantic chains: semantic similarity from a manually constructed resource, word association strength and semantic relatedness, both calculated from corpora.
Outcome:	The proposed classifiers outperform those that use a gold standard taxonomy for clinical conditions.

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Kris Prasad, Md Abdullah Al Hafiz Khan

Where do LLMs Encode the Knowledge to Assess the Ambiguity? (2025.coling-industry)

Challenge:	Federated learning with heterogeneous client architectures can be difficult due to semantic drift and poor inter-class separation.
Approach:	They propose a framework where heterogeneous clients exchange class prototypes with a central server and refine them through a geometric regularization objective.
Outcome:	The proposed framework achieves highest ensemble accuracy across all four image datasets and highest local test accuracy on low-class and clinical tasks.

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Hancheol Park, Geonmin Kim

Developing Prefix-Tuning Models for Hierarchical Text Classification (2022.emnlp-industry)

Challenge:	False sizing of large language models can generate unreliable responses .
Approach:	They propose a method to train large language models without ambiguity labels .
Outcome:	The proposed method detects ambiguous input prompts better than representations from the final layer.

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Lei Chen, Houwei Chou, Xiaodan Zhu

R&R: Metric-guided Adversarial Sentence Generation (2022.findings-aacl)

Challenge:	Hierarchical text classification (HTC) is a key task in many industrial applications. Pre-trained Language Models (PLMs) have become dominant for most natural language processing (NLP) tasks.
Approach:	They investigate how prefix tuning can improve hierarchical text classification . prefix-tuning model only needs less than 1% of parameters to achieve performance .
Outcome:	The proposed model can achieve comparable performance to regular full fine-tuning.

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Lei Xu, Alfredo Cuesta-Infante, Laure Berti-Equille, Kalyan Veeramachaneni

Hierarchical Text Classification with Reinforced Label Assignment (D19-1)

Challenge:	Existing methods prioritize misclassification by maximizing each perturbation’s effectiveness at misleading a text classifier.
Approach:	They propose a rewrite and rollback framework for adversarial attack that optimizes a critique score which combines fluency, similarity, and misclassification metrics.
Outcome:	The proposed framework outperforms current state-of-the-art in attack success rate by +16.2%, +12.8%, and +14.0% on the classifiers respectively.

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Yuning Mao, Jingjing Tian, Jiawei Han, Xiang Ren

MaskLID: Code-Switching Language Identification through Iterative Masking (2024.acl-short)

Challenge:	Existing hierarchical text classification methods make local decisions regarding labels or ignore hierarchy information during inference.
Approach:	They propose to learn a Label Assignment Policy via deep reinforcement learning to determine where to place an object and when to stop the assignment process.
Outcome:	The proposed method outperforms state-of-the-art methods on five datasets and four base models and achieves an average improvement of 33.4% over flat classifiers.

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Amir Hossein Kargaran, François Yvon, Hinrich Schuetze

Re-ranking Using Large Language Models for Mitigating Exposure to Harmful Content on Social Media Platforms (2025.acl-long)

Challenge:	Sentence-level LIDs are classifiers trained on monolingual texts to provide single labels, typically using a softmax layer to turn scores into probabilities.
Approach:	They propose a simple yet effective code-switching language identification method that uses the LID itself to mask features associated with L1 and L2 in the next round.
Outcome:	The proposed method is based on two open-source LIDs based in the FastText architecture and does not require any external resources.

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Rajvardhan Oak, Muhammad Haroon, Claire Wonjeong Jo, Magdalena Wojcieszak, Anshuman Chhabra

Lost in Space: Probing Fine-grained Spatial Understanding in Vision and Language Resamplers (2024.naacl-short)

Challenge:	Social media platforms use machine learning and artificial intelligence to maximize user engagement, but can indirectly cause exposure to harmful content.
Approach:	They propose a re-ranking approach using Large Language Models to assess and rerank content sequences using large annotated data sets.
Outcome:	The proposed method significantly outperforms existing proprietary moderation methods on three datasets, three models and across three configurations.

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Georgios Pantazopoulos, Alessandro Suglia, Oliver Lemon, Arash Eshghi

Mining Possessions: Existence, Type and Temporal Anchors (N18-1)

Challenge:	Recent approaches for developing vision and language models leverage existing vision and a language expert and try to learn a mapping between them.
Approach:	They propose to use a resampler module to create a ‘visual prompt’ which is then fed to the large language models (LLM) using a textual prompt.
Outcome:	The proposed method has been shown to be effective across coarse-grained tasks like image captioning and visual question answering, but more fine-grounded tasks that require spatial understanding have not been thoroughly examined.

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Dhivya Chinnappa, Eduardo Blanco

Challenge:	Existing annotations for possession relations can be used to predict possession existence, possession type and temporal anchors.
Approach:	They propose to use text annotations to mine possession relations from text . they assign temporal anchors indicating when possession holds between possessor and possessee .
Outcome:	The proposed task can predict possession existence, possession type and temporal anchors, and it can be automated.

Do NOT Classify and Count: Hybrid Attribute Control Success Evaluation (2026.eacl-long)

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Felix Matthias Saaro, Pius von Däniken, Mark Cieliebak, Jan Milan Deriu

Neural Event Semantics for Grounded Language Understanding (2021.tacl-1)

Challenge:	evaluating attribute control success in controllable text generation relies on pretrained classifiers.
Approach:	They propose a Bayesian method that combines classifier predictions with a small number of human labels for calibration.
Outcome:	The proposed method produces robust estimates across both text and image generation tasks, offering an alternative to current evaluation practices.

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Shyamal Buch, Li Fei-Fei, Noah D. Goodman

Building Adaptive Acceptability Classifiers for Neural NLG (2021.emnlp-main)

Challenge:	a new framework for compositional grounded language understanding is proposed . NES is trainable end-to-end by gradient descent with minimal supervision.
Approach:	They propose a conjunctivist framework for compositional grounded language understanding . they use words as classifiers that compose to form a sentence meaning by multiplying output scores .
Outcome:	The proposed framework improves on compositional grounded language tasks.

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Soumya Batra, Shashank Jain, Peyman Heidari, Ankit Arun, Catharine Youngs, Xintong Li, Pinar Donmez, Shawn Mei, Shiunzu Kuo, Vikas Bhardwaj, Anuj Kumar, Michael White

scRAG: Hybrid Retrieval-Augmented Generation for LLM-based Cross-Tissue Single-Cell Annotation (2025.findings-acl)

Challenge:	Existing approaches to generate synthetic data using simple sentence transformations and/or model-based techniques may not generate realistic error samples with respect to the NLG models.
Approach:	They propose a framework to train models to classify acceptability of responses generated by natural language generation models using a 2-stage approach . they use existing sentence transformations to generate samples that better resemble the output of the generation models.
Outcome:	The proposed approach outperforms existing techniques and can be used in few-shot settings using self-training.

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Zhiyin Yu, Chao Zheng, Chong Chen, Xian-Sheng Hua, Xiao Luo

Do we Name the Languages we Study? The #BenderRule in LREC and ACL articles (2022.lrec-1)

Challenge:	Large language models (LLMs) have demonstrated impressive potential in a wide range of fields, including biology, genomics and healthcare.
Approach:	They propose a framework that integrates advanced LLM-based RAG techniques into cross-tissue single-cell annotation.
Outcome:	The proposed framework outperforms baseline models, generalist models, domain-specific methods, and trained classifiers on a cross-tissue dataset.

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Fanny Ducel, Karën Fort, Gaël Lejeune, Yves Lepage

Challenge:	Using the #BenderRule, we examine the number and which languages are studied in two NLP conferences.
Approach:	They examine the application of the #BenderRule in NLP articles by inspecting 14,000 articles over a period of time ranging from 2000 to 2020 for LREC and 1979 to 2020 respectively.
Outcome:	The authors examine the application of the #BenderRule in natural language processing articles over a period of time ranging from 2000 to 2020 for LREC and ACL.

Joint Constrained Learning with Boundary-adjusting for Emotion-Cause Pair Extraction (2023.acl-long)

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Huawen Feng, Junlong Liu, Junhao Zheng, Haibin Chen, Xichen Shang, Qianli Ma

Filter-And-Refine: A MLLM Based Cascade System for Industrial-Scale Video Content Moderation (2025.acl-industry)

Challenge:	Emotion-Cause Pair Extraction (ECPE) aims to identify the document’s emotion clauses and corresponding cause clauses.
Approach:	They propose a constrained learning framework with boundary-adjusting for Emotion-Cause Pair Extraction that summarizes prior rules and forces the model to take them into consideration in optimization.
Outcome:	The proposed framework achieves competitive results compared with state-of-the-art methods on unbalanced data and proves robustness on unbalancing data.

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Zixuan Wang, Jinghao Shi, Hanzhong Liang, Xiang Shen, Vera Wen, Zhiqian Chen, Yifan Wu, Zhixin Zhang, Hongyu Xiong

Probing Multimodal Embeddings for Linguistic Properties: the Visual-Semantic Case (2020.coling-main)

Challenge:	Effective content moderation is essential for video platforms to safeguard user experience and uphold community standards.
Approach:	They propose a method to transform a generative MLLM into a multimodal classifier using minimal discriminative training data.
Outcome:	The proposed method improves F1 score by 66.50% over traditional classifiers while requiring only 2% of the fine-tuning data.

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Adam Dahlgren Lindström, Johanna Björklund, Suna Bensch, Frank Drewes

Visual Data in NLP

Challenge:	Semantic embeddings have advanced the state of the art for natural language processing tasks . but their inner workings are poorly understood and there is a shortage of analysis tools .
Approach:	They propose to extend visual-semantic embeddings to multimodal domains by defining probing tasks for embeddable image-caption pairs and testing them with classifiers.
Outcome:	The proposed probing tasks show up to 16% more accurate on visual-semantic embeddings compared to unimodal embedders . the proposed extensions to multimodal domains have been lauded as promising in natural language processing .

On the Role of Speech Data in Reducing Toxicity Detection Bias (2025.naacl-long)

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Samuel Bell, Mariano Coria Meglioli, Megan Richards, Eduardo Sánchez, Christophe Ropers, Skyler Wang, Adina Williams, Levent Sagun, Marta R. Costa-jussà

Estimating User Communication Styles for Spoken Dialogue Systems (2020.lrec-1)

Challenge:	Text toxicity detection systems produce disproportionate rates of false positives on demographic groups . toxicity classification systems often misinterpret benign group mentions as toxic .
Approach:	They use group annotations to compare text-based and speech-based toxicity detection systems.
Outcome:	The results show that access to speech data supports reduced bias against group mentions . the authors recommend improving classifiers, rather than transcription pipelines if possible .

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Juliana Miehle, Isabel Feustel, Julia Hornauer, Wolfgang Minker, Stefan Ultes

Understanding and Mitigating Spurious Correlations in Text Classification with Neighborhood Analysis (2024.findings-eacl)

Challenge:	a neural network estimation system for spoken dialogues can be used to estimate the communication style of a user's interaction, but this is rarely implemented in a live system.
Approach:	They propose a neural network approach to estimate the communication style of spoken interaction, namely elaborateness and directness.
Outcome:	The proposed method can estimate the elaborateness and directness of spoken interaction and improve the results with additional linguistic features.

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Oscar Chew, Hsuan-Tien Lin, Kai-Wei Chang, Kuan-Hao Huang

Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations (N18-2)

Challenge:	Recent research has revealed that machine learning models have a tendency to leverage spurious correlations that exist in the training set but may not hold true in general circumstances.
Approach:	They propose a metric to detect spurious tokens and a family of regularization methods to mitigate spurious correlations in text classification.
Outcome:	The proposed method prevents spurious clusters and significantly improves the robustness of classifiers without auxiliary data.

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Sosuke Kobayashi

Examining Spanish Counseling with MIDAS: a Motivational Interviewing Dataset in Spanish (2025.naacl-short)

Challenge:	Neural network-based models for NLP have been growing with state-of-the-art results in various tasks.
Approach:	They propose a data augmentation method for labeled sentences called contextual augmentation.
Outcome:	The proposed method improves classifiers based on convolutional or recurrent neural networks.

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Aylin Ece Gunal, Bowen Yi, John D. Piette, Rada Mihalcea, Veronica Perez-Rosas

Challenge:	Cultural and language factors influence counseling, but research has not explored whether this applies to other languages.
Approach:	They introduce a Spanish-language counseling dataset that contains expert annotations for counseling reflections and questions.
Outcome:	The proposed dataset explores language-based differences in counselor behavior in English and Spanish and develops classifiers in monolingual and multilingual settings.

The Promises and Pitfalls of LLM Annotations in Dataset Labeling: a Case Study on Media Bias Detection (2025.findings-naacl)

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Tomáš Horych, Christoph Mandl, Terry Ruas, Andre Greiner-Petter, Bela Gipp, Akiko Aizawa, Timo Spinde

Point-of-Interest Type Inference from Social Media Text (2020.aacl-main)

Challenge:	Recent research suggests using Large Language Models (LLMs) to automate the annotation process, reducing these costs while maintaining data quality.
Approach:	They propose to use Large Language Models to automate annotation process and train classifiers on large datasets.
Outcome:	The proposed model outperforms all of the annotator LLMs on two media bias benchmark datasets (BABE and BASIL) while maintaining data quality.

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Danae Sánchez Villegas, Daniel Preotiuc-Pietro, Nikolaos Aletras

SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check (2020.acl-main)

Challenge:	Using a dataset of 200,000 English tweets, we can predict the type of the place from which a tweet was sent from.
Approach:	They propose to analyze a dataset of 200,000 tweets from 2,761 points-of-interest in the U.S. and train classifiers to predict the type of the location a tweet was sent from.
Outcome:	The proposed method can predict the type of the location a tweet was sent from and reach a macro F1 of 43.67 across eight classes.

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Xingyi Cheng, Weidi Xu, Kunlong Chen, Shaohua Jiang, Feng Wang, Taifeng Wang, Wei Chu, Yuan Qi

Clarifying the Path to User Satisfaction: An Investigation into Clarification Usefulness (2024.findings-eacl)

Challenge:	Existing methods to detect and correct spelling errors in Chinese take external input or just heuristic rules.
Approach:	They propose to incorporate phonological and visual similarity knowledge into Chinese language models by using a specialized graph convolutional network.
Outcome:	The proposed method outperforms existing models on three human-annotated datasets.

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Hossein A. Rahmani, Xi Wang, Mohammad Aliannejadi, Mohammadmehdi Naghiaei, Emine Yilmaz

Placing M-Phasis on the Plurality of Hate: A Feature-Based Corpus of Hate Online (2022.lrec-1)

Challenge:	Poorly formulated questions can lead to user frustration and dissatisfaction .
Approach:	They propose to leverage key features that contribute to the classification of clarifying questions, enhancing user satisfaction and system performance.
Outcome:	The proposed model improves with a minimum performance boost of 45% in traditional classifiers, especially in large language models.

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Dana Ruiter, Liane Reiners, Ashwin Geet D’Sa, Thomas Kleinbauer, Dominique Fohr, Irina Illina, Dietrich Klakow, Christian Schemer, Angeliki Monnier

Ethical NLP

Challenge:	HS-related corpora over-simplify the phenomenon of hate by labelling user content with binary classes, e.g., hate/neutral . this ignores the complex and subjective nature of HS, which limits the real-life applicability of classifiers trained on these corporales.
Approach:	They present a corpus of 9k German and french user comments from migration-related news articles.
Outcome:	The proposed corpus is annotated with 23 features that become descriptors of various types of speech, ranging from critical comments to implicit and explicit expressions of hate.

Efficiently Aligned Cross-Lingual Transfer Learning for Conversational Tasks using Prompt-Tuning (2024.findings-eacl)

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Lifu Tu, Jin Qu, Semih Yavuz, Shafiq Joty, Wenhao Liu, Caiming Xiong, Yingbo Zhou

Identifying Transferable Information Across Domains for Cross-domain Sentiment Classification (P18-1)

Challenge:	Cross-lingual transfer of language models trained on high-resource languages such as English has been limited due to the high cost of obtaining non-English conversational data.
Approach:	They introduce a parallel and large-scale multilingual conversation dataset that is used for cross-lingual alignment pretraining by translating the English-only Schema-Guided Dialogue dataset into 105 other languages.
Outcome:	The proposed model performs well on slot-filling and intent classification tasks, and is able to perform well in other languages.

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Raksha Sharma, Pushpak Bhattacharyya, Sandipan Dandapat, Himanshu Sharad Bhatt

BrainPredict: a Tool for Predicting and Visualising Local Brain Activity (2020.lrec-1)

Challenge:	Cross-domain sentiment classification is challenging due to polarity orientation and significance differences . supervised learning algorithms have to be re-trained on every new domain .
Approach:	They propose that words that do not change their polarity and significance represent transferable information across domains for cross-domain sentiment classification.
Outcome:	The proposed method improves cross-domain sentiment classification performance by identifying polarity-preserving significant words across domains.

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Youssef Hmamouche, Laurent Prévot, Magalie Ochs, Thierry Chaminade

MiST: a Large-Scale Annotated Resource and Neural Models for Functions of Modal Verbs in English Scientific Text (2022.findings-emnlp)

Challenge:	Using fMRI, we recorded a corpus of human-human and human-robot conversations while participants brain activity was recorded with f.MRI, but we did not find any tools for displaying together brain activity prediction of non-controlled conversations, the raw material used in this prediction and the features used for these predictions.
Approach:	They propose a tool that allows dynamic prediction and visualization of an individual’s local brain activity during a conversation using raw behavioral data.
Outcome:	The proposed tool takes as input behavioral features computed from raw data, mainly the participant and the interlocutor speech but also the participant’s visual input and eye movements.

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Sophie Henning, Nicole Macher, Stefan Grünewald, Annemarie Friedrich

RewardBench: Evaluating Reward Models for Language Modeling (2025.findings-naacl)

Challenge:	modal verbs are used for hedges, but they may also denote abilities and restrictions in scientific texts . modals are often used for hedging, but prior work on this topic has been limited .
Approach:	They propose a dataset that contains 3737 modal instances in five scientific domains . they evaluate a set of competitive neural architectures to model the distinctions in MIST .
Outcome:	The proposed dataset contains 3737 modal instances in five scientific domains . leveraging non-scientific data is of limited benefit for modeling the distinctions in MIST .

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Nathan Lambert, Valentina Pyatkin, Jacob Morrison, Lester James Validad Miranda, Bill Yuchen Lin, Khyathi Chandu, Nouha Dziri, Sachin Kumar, Tom Zick, Yejin Choi, Noah A. Smith, Hannaneh Hajishirzi

SetConv: A New Approach for Learning from Imbalanced Data (2020.emnlp-main)

Challenge:	Evaluating reward models presents an opportunity to understand the opaque technologies used for alignment of language models.
Approach:	They present a benchmark dataset and code-base for evaluation of reward models . they use prompt-chosen-rejected trios to benchmark how they perform on queries .
Outcome:	The proposed dataset compares RMs with other models on a set of questions.

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Yang Gao, Yi-Fan Li, Yu Lin, Charu Aggarwal, Latifur Khan

Detecting Extraneous Content in Podcasts (2021.eacl-main)

Challenge:	Existing methods for classification are biased towards the majority class when the Imbalance Ratio (IR) is high.
Approach:	They propose a set convolution operation and an episodic training strategy to extract a single representative for each class, so that classifiers can later be trained on a balanced class distribution.
Outcome:	The proposed algorithm is permutation-invariant despite the order of inputs and shows superiority on multiple large-scale benchmark text datasets.

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Sravana Reddy, Yongze Yu, Aasish Pappu, Aswin Sivaraman, Rezvaneh Rezapour, Rosie Jones

Speech & Audio in NLP

Challenge:	Podcast episodes often contain extraneous material interleaved within the audio and the written descriptions . authors present classifiers that leverage both textual and listening patterns to detect such content .
Approach:	They propose a classifier that leverages both textual and listening patterns to detect extraneous material in podcast descriptions and audio transcripts.
Outcome:	The proposed classifiers improve ROUGE scores and reduce extraneous content in podcast summarization tasks.

Breaking the Ceiling of the LLM Community by Treating Token Generation as a Classification for Ensembling (2024.findings-emnlp)

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Yao-Ching Yu, Chun Chih Kuo, Ye Ziqi, Chang Yucheng, Yueh-Se Li

Resource Creation Towards Automated Sentiment Analysis in Telugu (a low resource language) and Integrating Multiple Domain Sources to Enhance Sentiment Prediction (L18-1)

Challenge:	Large Language Models (LLMs) have demonstrated remarkable capabilities in a wide range of tasks and contexts.
Approach:	They propose to use a token-level ensembling method to exploit the probability information at each generation step and to avoid early incorrect tokens.
Outcome:	The proposed method breaks the existing community performance ceiling and improves on several benchmarks.

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Rama Rohit Reddy Gangula, Radhika Mamidi

Beyond Model Interpretability: On the Faithfulness and Adversarial Robustness of Contrastive Textual Explanations (2022.findings-emnlp)

Challenge:	Sentiment Analysis of text is an important task in many applications . but the task becomes challenging when it comes to low resource languages .
Approach:	They propose to create a corpus of polarity-based sentiment classifiers in Telugu for different domains like movie reviews, song lyrics, product reviews and book reviews.
Outcome:	The proposed model performs well in multiple domains and is compared with the previous models.

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Julia El Zini, Mariette Awad

Randomized Deep Structured Prediction for Discourse-Level Processing (2021.eacl-main)

Challenge:	Existing explanations address the contrastive aspect of explanations but their extension to textual data is under-explored and there is little investigation on their vulnerabilities and limitations.
Approach:	They propose a novel evaluation scheme inspired by the faithfulness of explanations by extending the computation of three metrics to textual data and benchmarking POLYJUICE and MiCE on suggested metrics.
Outcome:	The proposed methods demonstrate that the connectedness of counterfactuals to their original counterparts is not obvious in both models.

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Manuel Widmoser, Maria Leonor Pacheco, Jean Honorio, Dan Goldwasser

Co-training an Unsupervised Constituency Parser with Weak Supervision (2022.findings-acl)

Challenge:	Expressive text encoders have been at the center of recent NLP work . however, some tasks require complex structural dependencies between texts .
Approach:	They propose to leverage deep structured prediction and expressive neural encoders for argumentation mining tasks.
Outcome:	The proposed framework can be used for argumentation mining tasks without expensive inference tools.

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Nickil Maveli, Shay Cohen

Generating Token-Level Explanations for Natural Language Inference (N19-1)

Challenge:	Existing methods for unsupervised parsing that use bootstrapping classifiers to identify if a node dominates a span are lacking.
Approach:	They propose a method for unsupervised parsing that relies on bootstrapping classifiers to identify if a node dominates a specific span.
Outcome:	The proposed method achieves 63.1 F1 on the English test set and new state-of-the-art on treebanks for Chinese and Japanese.

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James Thorne, Andreas Vlachos, Christos Christodoulopoulos, Arpit Mittal

Shiyuan Xiao, Yuanxin Ouyang, Wenge Rong, Jianxin Yang, Zhang Xiong

Challenge:	Existing methods to generate token-level explanations for NLI on single sentences have not been tested.
Approach:	They propose to generate token-level explanations for NLI without explicitly annotating training data.
Outcome:	The proposed approach is faster and more accurate than the black-box methods.

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Exploiting Class Probabilities for Black-box Sentence-level Attacks (2024.findings-eacl)

Challenge:	Named entity recognition (NER) tasks are a fundamental challenge for name recognition tasks that aim to reduce the boundary error when entities become longer.
Approach:	They propose a similarity based auxiliary classifier which can distinguish entity words from non-entity words by using vectors to indicate tags.
Outcome:	Empirical results show that the proposed classifier can perform better than baseline approaches.

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Raha Moraffah, Huan Liu

Multinomial Adversarial Networks for Multi-Domain Text Classification (N18-1)

Challenge:	Existing sentences use either no feedback or only the class labels for sentence-level attacks.
Approach:	They propose an algorithm that uses class probabilities for black-box sentence-level attacks and investigate the effectiveness of using class probabilties on the attack’s success.
Outcome:	The proposed algorithm is evaluated against baselines and classifiers and compares with the existing models to determine whether it is worthy or practical to use class probabilities for black-box sentence-level attacks.

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Xilun Chen, Claire Cardie

Efficient, Uncertainty-based Moderation of Neural Networks Text Classifiers (2022.findings-acl)

Challenge:	Existing methods for text classification are domain-dependent, but there is no annotated data for some domains.
Approach:	They propose a multinomial adversarial network to tackle multi-domain text classification . they show that MANs significantly outperform prior art on the MDTC task .
Outcome:	The proposed model outperforms the prior art on the multi-domain text classification task.

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Jakob Smedegaard Andersen, Walid Maalej

Cross-Target Stance Classification with Self-Attention Networks (P18-2)

Challenge:	A series of benchmarking experiments based on three different datasets and three state-of-the-art classifiers show that our framework can improve the classification F1-scores by 5.1 to 11.2% (up to approx. 98 to 99%)
Approach:	They propose a semi-automated approach that passes unconfident, probably incorrect classifications to human moderators to minimize the workload.
Outcome:	The proposed approach can improve the classification F1-scores by 5.1 to 11.2% (up to approx. 98 to 99%) while reducing the moderation load up to 73.3% compared to a random moderation.

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Chang Xu, Cécile Paris, Surya Nepal, Ross Sparks

An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction (D19-1)

Challenge:	In stance classification, the target on which the stance is made defines the boundary of the task, and a classifier is usually trained for prediction on the same target.
Approach:	They propose a neural model that can generalize classifiers between different targets by finding useful information shared between relevant targets.
Outcome:	The proposed model can generalize between relevant targets and find useful information shared between relevant target domains which improves generalization in certain scenarios.

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Stefan Larson, Anish Mahendran, Joseph J. Peper, Christopher Clarke, Andrew Lee, Parker Hill, Jonathan K. Kummerfeld, Kevin Leach, Michael A. Laurenzano, Lingjia Tang, Jason Mars

Co-training for Low Resource Scientific Natural Language Inference (2024.acl-long)

Challenge:	Task-oriented dialog systems need to know when a query falls outside their range of supported intents.
Approach:	They propose a dataset that includes queries that are out-of-scope and 150 intent classes over 10 domains.
Outcome:	The proposed dataset includes queries that are out-of-scope, i.e., queries that do not fall into any of the system’s supported intents.

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Mobashir Sadat, Cornelia Caragea

Meta-Information Guided Meta-Learning for Few-Shot Relation Classification (2020.coling-main)

Challenge:	Scientific Natural Language Inference (NLI) is a task to predict the semantic relation between sentences extracted from research articles.
Approach:	They propose a co-training method that assigns weights based on the training dynamics of the classifiers to the distantly supervised labels.
Outcome:	The proposed method achieves an improvement of 1.5% in Macro F1 over the distant supervision baseline and substantial improvements over several other strong SSL baselines.

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Bowen Dong, Yuan Yao, Ruobing Xie, Tianyu Gao, Xu Han, Zhiyuan Liu, Fen Lin, Leyu Lin, Maosong Sun

Teamwork Is Not Always Good: An Empirical Study of Classifier Drift in Class-incremental Information Extraction (2023.findings-acl)

Challenge:	Existing meta-learning models rely on implicit instance statistics and are unreliability and weak interpretability.
Approach:	They propose a meta-information guided meta-learning framework that uses semantics to guide meta- learning . experimental results demonstrate the effectiveness of the proposed framework .
Outcome:	The proposed framework can establish connections between instance-based information and semantic-based data, enabling faster initialization and adaptation.

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Minqian Liu, Lifu Huang

Inferring about fraudulent collusion risk on Brazilian public works contracts in official texts using a Bi-LSTM approach (2020.findings-emnlp)

Challenge:	Existing methods for learning incrementally do not address the problem of class-incremental learning.
Approach:	They propose a framework that can continuously learn new classes from a data stream without forgetting previously learned classes.
Outcome:	The proposed framework shows significant improvement over the state-of-the-art frameworks with up to 44.7% absolute F-score gain.

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Marcos Lima, Roberta Silva, Felipe Lopes de Souza Mendes, Leonardo R. de Carvalho, Aleteia Araujo, Flavio de Barros Vidal

Probe-Free Low-Rank Activation Intervention (2025.naacl-long)

Challenge:	Public works procurements are a preferred field for collusion and fraud in Brazil . current methods of fraud detection use structured data to classification and usually do not involve annotated data.
Approach:	They propose to use public works procurements to classify risky entries using a dataset of 15,132,968 textual entries of which 1,907 are annotated.
Outcome:	The proposed datasets show that both bottleneck deep neural network and biLSTM are competitive compared with classical classifiers and achieve better precision (93.0% and 92.4%, respectively).

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Chonghe Jiang, Bao Nguyen, Anthony Man-Cho So, Viet Anh Nguyen

Detecting cognitive impairments by agreeing on interpretations of linguistic features (N19-1)

Challenge:	Existing activation intervention methods involve an activation probe to detect undesirable generation, triggering the activation modification to steer subsequent generation.
Approach:	They propose a probe-free intervention method FLORAIN for all attention heads in a specific activation layer.
Outcome:	The proposed method outperforms baseline methods in enhancing model truthfulness and quality across generation and multiple-choice tasks.

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Zining Zhu, Jekaterina Novikova, Frank Rudzicz

Explainability & Interpretability in NLP

Challenge:	Linguistic features have shown promising applications for detecting cognitive impairments.
Approach:	They propose a framework to classify after reaching agreements between modalities by using linguistic features to divide linguistic subsets into subset and let neural networks learn low-dimensional representations that agree with each other.
Outcome:	The proposed framework outperforms existing classifiers using all of the 413 linguistic features.

Better as Generators Than Classifiers: Leveraging LLMs and Synthetic Data for Low-Resource Multilingual Classification (2026.findings-eacl)

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Branislav Pecher, Jan Cegin, Robert Belanec, Ivan Srba, Jakub Simko, Maria Bielikova

What Causes the Failure of Explicit to Implicit Discourse Relation Recognition? (2024.naacl-long)

Challenge:	Large Language Models (LLMs) have demonstrated remarkable multilingual capabilities, making them promising tools in both high- and low-resource languages.
Approach:	They use a multilingual LLM to generate synthetic datasets covering 11 languages and 4 classification tasks and use them to train smaller models.
Outcome:	The proposed model outperforms the large generator in low-resource languages and tasks.

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Wei Liu, Stephen Wan, Michael Strube

Multi-View Source Ablation for Faithful Summarization (2023.findings-eacl)

Challenge:	Prior work claimed that explicit classifiers perform poorly in implicit scenarios . a label shift occurs after connectives are removed, but no empirical evidence supports this claim .
Approach:	They propose to prove that the discourse relations expressed by some explicit instances will change when connectives disappear.
Outcome:	The proposed methods outperform strong baselines on PDTB 2.0, PDTT 3.0, and the GUM dataset.

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Shuyang Cao, Liang Ma, Di Lu, Robert L Logan IV, Joel Tetreault, Alejandro Jaimes

Summarization

Challenge:	MuFaSSa is a metric for evaluating faithfulness of abstractive summaries . it uses different strategies to remove information from source document to form multiple ablated views .
Approach:	They propose a metric for evaluating faithfulness of abstractive summaries using multiple ablated views.
Outcome:	The proposed metric outperforms existing models on summarization tasks and human-annotated faithfulness labels.

The Subject Annotations of the Danish Parliament Corpus (2009-2017) - Evaluated with Automatic Multi-label Classification (2022.lrec-1)

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Costanza Navarretta, Dorte Haltrup Hansen

LEDGAR: A Large-Scale Multi-label Corpus for Text Classification of Legal Provisions in Contracts (2020.lrec-1)

Challenge:	The interest in analysing and automatically processing large amounts of political data has increased in the past decades.
Approach:	They address the semi-automatic annotation of subjects in the Danish Parliament Corpus (2009-2017) v.2 and describe multi-label classification experiments to verify the consistency of the subject annotation.
Outcome:	The proposed method improves on the baseline classifier, which is a majority classifier.

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Don Tuggener, Pius von Däniken, Thomas Peetz, Mark Cieliebak

Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification (2022.acl-long)

Challenge:	Contractual provisions are a primary research target in law studies as they constitute the legal essence of a contract.
Approach:	They propose to use LEDGAR to construct a multilabel corpus of legal provisions in contracts that is crawled and scraped from the public domain.
Outcome:	The proposed corpus is the first freely available corpus of its kind.

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Shengding Hu, Ning Ding, Huadong Wang, Zhiyuan Liu, Jingang Wang, Juanzi Li, Wei Wu, Maosong Sun

A Multi- versus a Single-classifier Approach for the Identification of Modality in the Portuguese Language (L18-1)

Challenge:	Recent studies suggest that pre-trained language models have gained rich knowledge during pre-training.
Approach:	They propose to tune pre-trained language models with task-specific prompts to improve and stabilize prompttuning.
Outcome:	Extensive experiments on zero and few-shot text classification tasks show that prompt-tuning improves and stabilizes prompttun-ing.

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João Sequeira, Teresa Gonçalves, Paulo Quaresma, Amália Mendes, Iris Hendrickx

Automatic Animacy Classification for Romanian Nouns (2024.lrec-main)

Challenge:	Comparative study of two different approaches to build an automatic classification system for Modality values in the Portuguese language.
Approach:	They propose to use a single multi-class classifier with the full Portuguese language dataset that includes eleven modal verbs and a weighted average approach to build different classifiers for each verb.
Outcome:	The proposed system is based on a Portuguese language dataset with 11 modal verbs and two different classifiers, one for each verb.

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Maria Tepei, Jelke Bloem

Detecting de minimis Code-Switching in Historical German Books (2020.coling-main)

Challenge:	Animacy is a semantic property of nouns that describes the quality of the noun's referent of being alive, sentient or volitional.
Approach:	They propose a type-based binary classifier of Romanian nouns into the classes human/non-human using pre-trained word embeddings and animacy information derived from Romanian WordNet.
Outcome:	The proposed classifiers perform well on the Romanian language and in a naturalistic setting.

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Shijia Liu, David Smith

Enhancing Modern Supervised Word Sense Disambiguation Models by Semantic Lexical Resources (L18-1)

Challenge:	Code-switching has drawn scholarly attention in computational linguistics and natural language processing from many different perspectives.
Approach:	They propose to compare informal code-switching to its appearance in more formal registers by annotating and inspecting the German textarchives.
Outcome:	The proposed classifiers can help reduce errors when speech recognition is applied to a large corpus with rare embedded languages.

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Stefano Melacci, Achille Globo, Leonardo Rigutini

Automatic Authorship Analysis in Human-AI Collaborative Writing (2024.lrec-main)

Challenge:	Existing supervised models for Word Sense Disambiguation (WSD) are limited to knowledge-based approaches.
Approach:	They propose to use WordNet and WordNet Domains to enhance supervised WSD models by introducing semantic features into the classifiers and using the SLR structure to augment training data.
Outcome:	The proposed model improves the state-of-the-art in Word Sense Disambiguation (WSD) The proposed approach is compared with the state of the art in the most popular benchmarks.

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Aquia Richburg, Calvin Bao, Marine Carpuat

Human Needs Categorization of Affective Events Using Labeled and Unlabeled Data (N18-1)

Challenge:	Existing methods for authorship analysis and text detection are limited . authors: human-AI collaborative writing poses a potential challenge for existing methods .
Approach:	They investigate the extent to which existing AI detection and authorship analysis models can perform classification on data generated in human-AI collaborative writing sessions.
Outcome:	The proposed models outperform existing models on human-AI collaborative writing data . authors say human- AI co-written text will require adapting models in the near future .

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Haibo Ding, Ellen Riloff

Framing Unpacked: A Semi-Supervised Interpretable Multi-View Model of Media Frames (2021.naacl-main)

Challenge:	Recent research has focused on identifying affective events in text, which are activities or states that positively or negatively affect the people who experience them.
Approach:	They propose to categorize affective events based upon human need categories that often explain people’s motivations and desires: PHYSIOLOGICAL, HEALTH, LEISURE, SOCIAL, FINANCIAL, COGNITION, and FREEDOM.
Outcome:	The proposed model learns from unlabeled data and produces significantly better results than individual classifiers.

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Shima Khanehzar, Trevor Cohn, Gosia Mikolajczak, Andrew Turpin, Lea Frermann

The Trumpiest Trump? Identifying a Subject’s Most Characteristic Tweets (D19-1)

Challenge:	Existing models for news analysis lack transparency in their predictions.
Approach:	They propose a semi-supervised model that embeds local information into news articles . it can be used to improve automatic news analysis, authors argue .
Outcome:	The proposed model outperforms previous models and can be used with unlabeled training data.

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Charuta Pethe, Steve Skiena

Training Classifiers with Natural Language Explanations (P18-1)

Challenge:	characterization scores are associated with popularity of a given short text, but are not always representative of the source.
Approach:	They use a dataset of tweets from 15 celebrities to quantify the extent to which a given short text is characteristic of a specific person.
Outcome:	The proposed model shows a statistically significant correlation between characterization scores and popularity of the associated texts for 13 of the 15 celebrities in the study.

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Braden Hancock, Paroma Varma, Stephanie Wang, Martin Bringmann, Percy Liang, Christopher Ré

DecOp: A Multilingual and Multi-domain Corpus For Detecting Deception In Typed Text (2020.lrec-1)

Challenge:	a semantic parser converts explanations into programmatic labeling functions . a standard protocol for obtaining a labeled dataset provides only one bit of information per example .
Approach:	They propose a framework where an annotator provides an explanation for each labeling decision . they use a semantic parser to convert these explanations into programmatic labeling functions .
Outcome:	The proposed framework trains classifiers faster by providing explanations instead of labels . the proposed framework is based on a rule-based semantic parser .

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Pasquale Capuozzo, Ivano Lauriola, Carlo Strapparava, Fabio Aiolli, Giuseppe Sartori

What do tokens know about their characters and how do they know it? (2022.naacl-main)

Challenge:	Recent studies show that humans are ineffective in spotting deceit, with accuracy rates only slightly above the chance level.
Approach:	They propose a new language resource for automatic deception detection in cross-domain and cross-language scenarios.
Outcome:	The proposed language resource is composed of 5000 examples of truthful and deceitful first-person opinions across five different domains and two languages.

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Ayush Kaushal, Kyle Mahowald

Scaling Rich Style-Prompted Text-to-Speech Datasets (2025.emnlp-main)

Challenge:	Pre-trained language models that use subword tokenization schemes can succeed at a variety of language tasks that require character-level information.
Approach:	They propose to use word tokenization schemes to probe what word pieces encode . they show that larger models can encode character-level information .
Outcome:	The proposed models can encode character-level information and perform better on non-Latin alphabets.

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Anuj Diwan, Zhisheng Zheng, David Harwath, Eunsol Choi

Lifelong Learning of Hate Speech Classification on Social Media (2021.naacl-main)

Challenge:	Existing datasets that only cover basic tags are limited in their scale or coverage of style tags.
Approach:	They propose a large-scale dataset that annotates speech utterances with rich style captions.
Outcome:	The proposed dataset scales speech utterances with rich style captions for the first time.

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Jing Qian, Hong Wang, Mai ElSherief, Xifeng Yan

Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings (2021.emnlp-main)

Challenge:	Existing work on automated hate speech classification assumes that the dataset is fixed and the classes are pre-defined.
Approach:	They propose to use Variational Representation Learning and a load-balancing self-organizing inductive neural network to learn hate speech classification on social media.
Outcome:	The proposed model improves on the lifelong learning techniques on social media.

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Shaopeng Lai, Ante Wang, Fandong Meng, Jie Zhou, Yubin Ge, Jiali Zeng, Junfeng Yao, Degen Huang, Jinsong Su

Structured Data in NLP

Challenge:	Existing sentence ordering models can be classified into pairwise ordering models and set-to-sequence models.
Approach:	They propose a novel sentence ordering framework which introduces two classifiers to make better use of pairwise orderings for graph-based sentence ordering.
Outcome:	The proposed model achieves state-of-the-art performance on five commonly-used datasets.

Validating Automatic Evaluation of Controllable Counterspeech Generation: Rankings Matter More Than Scores (2026.eacl-long)

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Yi Zheng, Björn Ross, Walid Magdy

Don’t sweat the small stuff, classify the rest: Sample Shielding to protect text classifiers against adversarial attacks (2022.naacl-main)

Challenge:	Existing methods for evaluating attributes of counterspeech are limited and the validity of such evaluations is questionable when the classifiers themselves have only modest performance.
Approach:	They examine the automatic evaluation of counterspeech attributes using a multi-attribute counterseech dataset containing 2,728 samples.
Outcome:	The proposed model can be trusted by classifier validation, and it can rank models with confidence.

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Jonathan Rusert, Padmini Srinivasan

IruMozhi: Automatically classifying diglossia in Tamil (2024.findings-naacl)

Challenge:	Current text classifiers are subject to adversarial attacks from adversaries, typically executed using machine learning methods.
Approach:	They propose a novel and intuitive defense strategy called Sample Shielding that is attacker and classifier agnostic and does not require reconfiguration of the classifier or external resources.
Outcome:	The proposed defense is attacker and classifier agnostic and does not require reconfiguration of the classifier or external resources and is simple to implement.

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Kabilan Prasanna, Aryaman Arora

Retrieval as Generation: A Unified Framework with Self-Triggered Information Planning (2026.acl-long)

Challenge:	Literary Tamil is highly diglossic, with two very different registers in everyday use . Spoken Tamil is under-studied in modern NLP systems compared to Literary Tamil written in the Tamil script .
Approach:	They present a human-translated dataset of parallel text in Literary and Spoken Tamil.
Outcome:	The proposed model trains classifiers on the task of identifying which Tamil variety a text belongs to.

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Bo Li, Mingda Wang, Gexiang Fang, Shikun Zhang, Wei Ye

Learning with Latent Language (N18-1)

Challenge:	Existing models that ground retrieval on external evidence are limited in their ability to implement retrieval-augmented generation.
Approach:	They propose a retrieval-augmented generation model that embeds retrieval control directly into generation.
Outcome:	The proposed model surpasses strong RAG baselines and uses substantially fewer parameters.

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Jacob Andreas, Dan Klein, Sergey Levine

BigNLI: Native Language Identification with Big Bird Embeddings (2024.lrec-main)

Challenge:	Using the space of natural language strings as a parameter space is an effective way to capture natural task structure.
Approach:	They propose to use natural language as a parameter space for few-shot learning problems including classification, transduction and policy search.
Outcome:	The proposed model outperforms models with a linguistic parameterization on image classification, text editing, and reinforcement learning.

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Sergey Kramp, Giovanni Cassani, Chris Emmery

Robots-Dont-Cry: Understanding Falsely Anthropomorphic Utterances in Dialog Systems (2022.emnlp-main)

Challenge:	Native Language Identification (NLI) is a task that relies on time-consuming linguistic feature engineering and current transformer models are limited by input size.
Approach:	They propose to train a logistic regression classifier which only uses Big Bird embeddings to overcome this limitation.
Outcome:	The proposed method outperforms linguistic feature engineering models on the Reddit-L2 dataset and shows consistent out-of-sample and out-off-domain performance.

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David Gros, Yu Li, Zhou Yu

Deep Subjecthood: Higher-Order Grammatical Features in Multilingual BERT (2021.eacl-main)

Challenge:	Dialog systems often output human-like responses, but some are impossible for a machine to say.
Approach:	They collect ratings on the feasibility of 900 two-turn dialogs from 9 data sources . they build classifiers and explore how modeling configuration might affect output permissibly .
Outcome:	The proposed model can be used to train human-like dialogs, but it is not anthropomorphic.

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Isabel Papadimitriou, Ethan A. Chi, Richard Futrell, Kyle Mahowald

Improving homograph disambiguation with supervised machine learning (L18-1)

Challenge:	a recent study has shown that multilingual BERT encodes sentences in structurally meaningful ways.
Approach:	They analyze how morphosyntactic alignment manifests across embedding spaces of languages . they train classifiers to recover subjecthood of mBERT embedds in transitive sentences .
Outcome:	The proposed model encodes a high-order grammatical feature of morphosyntactic alignment across languages . the results show that the classifier distributions reflect the morphological alignment of their training languages based on the results .

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Kyle Gorman, Gleb Mazovetskiy, Vitaly Nikolaev

Incubating Text Classifiers Following User Instruction with Nothing but LLM (2024.emnlp-main)

Challenge:	a new system for text-to-speech synthesis uses rule-based homograph disambiguation . a simple application of machine learning produces significant improvements in homograph ambiguity .
Approach:	They propose a rule-based homograph disambiguation system for text-to-speech synthesis at Google . they compare it to a new system which performs disambiguations using classifiers trained on labeled data .
Outcome:	The proposed system is more accurate than hand-written rules or machine learning alone.

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Letian Peng, Zilong Wang, Jingbo Shang

Document-Level Event Argument Extraction by Leveraging Redundant Information and Closed Boundary Loss (2022.naacl-main)

Challenge:	In this paper, we aim to generate text classification data given arbitrary class definitions . Traditional supervised text classification fine-tunes models on expensive human annotation .
Approach:	They propose a framework that can generate text classification data given arbitrary class definitions . they use instruction-to-data mappings and in-context augmentation to refine the framework .
Outcome:	The proposed framework outperforms existing methods on benchmarks and training data generation by prompt engineering.

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Hanzhang Zhou, Kezhi Mao

Suum Cuique: Studying Bias in Taboo Detection with a Community Perspective (2022.findings-acl)

Challenge:	Document-level event argument extraction is a crucial subtask of event extraction.
Approach:	They propose to use redundant event information to extract multiple arguments from a document . they propose a loss function to classify Universum class by their open decision boundary .
Outcome:	The proposed model outperforms the previous state-of-the-art models by 3.35% in F1-score.

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Osama Khalid, Jonathan Rusert, Padmini Srinivasan

Cross-Domain Sentiment Classification with Target Domain Specific Information (P18-1)

Challenge:	Prior research has shown the need to consider community language norms when studying taboo text classification and annotations.
Approach:	They propose to use special classifiers tuned for each community's language to study bias in taboo classification and annotation where a community perspective is front and center.
Outcome:	The proposed method shows that biases are strongest against African Americans and South Asians . a community perspective is front and center in the proposed method .

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Minlong Peng, Qi Zhang, Yu-gang Jiang, Xuanjing Huang

GLoHBCD: A Naturalistic German Dataset for Language of Health Behaviour Change on Online Support Forums (2022.lrec-1)

Challenge:	Existing methods for sentiment classification focus on learning domain-invariant representations . few of them pay attention to domain-specific information, which should also be informative.
Approach:	They propose a method to extract domain specific and invariant representations and train a classifier on each of them.
Outcome:	The proposed model can achieve better performance than state-of-the-art methods.

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Selina Meyer, David Elsweiler

Natural Language Interfaces

Challenge:	Existing motivational interviewing methods lack the deep understanding of user utterances that is essential to the spirit of motivational interviews.
Approach:	They propose to use a German dataset of naturalistic language around health behaviour change to examine the motivational state of the user.
Outcome:	The proposed dataset of naturalistic language around health behaviour change is based on a weight loss forum in germany and is evaluated using theoretically grounded motivational interviewing categories.

Assessing the Human Likeness of AI-Generated Counterspeech (2025.coling-main)

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Xiaoying Song, Sujana Mamidisetty, Eduardo Blanco, Lingzi Hong

Challenge:	Existing studies have focused on relevance, surface form, and other shallow linguistic characteristics.
Approach:	They propose to evaluate the human likeness of AI-generated counterspeech . they implement and evaluate several LLM-based generation strategies .
Outcome:	The proposed models show that human-written counterspeech can be distinguished by both simple classifiers and humans.

iACOS: Advancing Implicit Sentiment Extraction with Informative and Adaptive Negative Examples (2024.naacl-long)

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Xiancai Xu, Jia-Dong Zhang, Lei Xiong, Zhishang Liu

LLM-based Rewriting of Inappropriate Argumentation using Reinforcement Learning from Machine Feedback (2024.acl-long)

Challenge:	Existing methods for extracting aspects and opinions from text are incomplete.
Approach:	They propose a method for extracting Implicit Aspects with Categories and Opinions with Sentiments using implicit tokens.
Outcome:	The proposed method outperforms baseline methods on two public benchmark datasets.

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Timon Ziegenbein, Gabriella Skitalinskaya, Alireza Bayat Makou, Henning Wachsmuth

Text Generation

Challenge:	Creating trusted and safe online spaces for people with different backgrounds and opinions is a challenge for social media platforms.
Approach:	They propose a reinforcement learning-based rewriting approach that balances content preservation and appropriateness based on existing classifiers.
Outcome:	The proposed approach significantly outperforms baselines including few-shot learning, prompting, and humans.

A Benchmark on Extremely Weakly Supervised Text Classification: Reconcile Seed Matching and Prompting Approaches (2023.findings-acl)

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Zihan Wang, Tianle Wang, Dheeraj Mekala, Jingbo Shang

Can Humans Identify Domains? (2024.lrec-main)

Challenge:	Existing methods for XWS-TC rely on minimal human guidance . X-WS-tc methods require no humanannotated datasets .
Approach:	They propose a benchmarking method to compare two approaches to XWS-TC . they use seed-matching and prompting a language model with instructions to decode label words .
Outcome:	The proposed methods are more tolerant to human guidance and more robust to model-based methods.

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Maria Barrett, Max Müller-Eberstein, Elisa Bassignana, Amalie Brogaard Pauli, Mike Zhang, Rob van der Goot

Joint Learning of Hyperbolic Label Embeddings for Hierarchical Multi-label Classification (2021.eacl-main)

Challenge:	Textual domain is a crucial property within the Natural Language Processing community due to its effects on downstream model performance.
Approach:	They examine the level of human disagreement and the relative difficulty of each annotation task by training classifiers to perform the same task.
Outcome:	The authors show that human proficiency in identifying related intrinsic textual properties is low and that disagreements are high.

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Soumya Chatterjee, Ayush Maheshwari, Ganesh Ramakrishnan, Saketha Nath Jagarlapudi

Multilingual Supervision Improves Semantic Disambiguation of Adpositions (2025.coling-main)

Challenge:	Existing methods for hierarchical multi-label classification do not assume label hierarchy exists.
Approach:	They propose to jointly learn the classifier parameters as well as the label embeddings . they propose to use hyperbolic embeddables to gain better generalisation over the labels .
Outcome:	The proposed method achieves state-of-the-art generalization on benchmarks and is more accurate than existing methods.

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Wesley Scivetti, Lauren Levine, Nathan Schneider

Re-embedding Difficult Samples via Mutual Information Constrained Semantically Oversampling for Imbalanced Text Classification (2021.emnlp-main)

Challenge:	a corpus-based cross-linguistic investigation into the lexical semantics of adpositions is conducted . a significant amount of ambiguity and flexibility in their meanings are present in a variety of languages .
Approach:	They conduct a corpus-based corpus analysis of adpositions using SNACS . they find distributional differences in a language's adequacy and disambiguation performance .
Outcome:	The proposed framework is suited for analyzing adpositions across languages . it provides a framework for a wide-coverage corpus annotation of high-level senses .

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Jiachen Tian, Shizhan Chen, Xiaowang Zhang, Zhiyong Feng, Deyi Xiong, Shaojuan Wu, Chunliu Dou

Answer-state Recurrent Relational Network (AsRRN) for Constructed Response Assessment and Feedback Grouping (2023.findings-emnlp)

Challenge:	Existing frameworks for imbalanced text classification can generate anchor instances for difficult samples . difficult samples are hard to classify as they are embedded into an overlapping semantic region with the majority class.
Approach:	They propose a Mutual Information constrained Semantically Oversampling framework that generates anchor instances for difficult samples to help the backbone network determine the re-embedding position of a non-overlapping representation.
Outcome:	The proposed framework can generate anchor instances to help classifiers achieve significant improvements over baselines on a variety of imbalanced text classification tasks.

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Zhaohui Li, Susan Lloyd, Matthew Beckman, Rebecca Passonneau

Challenge:	STEM educators must trade off the ease of assessing selected response (SR) questions with constructed response (CR) questions, where students articulate their own reasoning.
Approach:	They propose to use multiple choice questions to construct response questions . they use a relational neural network to relate context, questions, reference responses, and students’ answers to a new dataset of 6,532 student responses to three, two-part CR questions.
Outcome:	The proposed network outperforms classifiers based on LLMs and few-shot learning with GPT-3.5 on a new dataset of 6,532 student responses to three, two-part CR questions.

Quantifying Generative Media Bias with a Corpus of Real-world and Generated News Articles (2024.findings-emnlp)

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Filip Trhlík, Pontus Stenetorp

From the Detection of Toxic Spans in Online Discussions to the Analysis of Toxic-to-Civil Transfer (2022.acl-long)

Challenge:	Existing studies focus on LLMs undertaking political questionnaires, which offers only limited insights into their biases and operational nuances.
Approach:	They propose to use a curated dataset to generate 56,700 synthetic articles using nine LLMs.
Outcome:	The proposed model can detect political biases using supervised models and LLMs.

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John Pavlopoulos, Leo Laugier, Alexandros Xenos, Jeffrey Sorensen, Ion Androutsopoulos

Challenge:	a dataset of English posts with annotations of toxic spans is released . sequence labeling models perform best, but rationale extraction methods are promising .
Approach:	They propose a dataset for toxic spans detection that includes an annotation of toxic posts . they propose to add generic rationale extraction mechanisms to the model to obtain toxic span information .
Outcome:	The proposed framework is based on a dataset of English posts with toxic span annotations . it shows that sequence labeling models perform best, but that rationale extraction methods are promising .

Revisiting Implicitly Abusive Language Detection: Evaluating LLMs in Zero-Shot and Few-Shot Settings (2025.coling-main)

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Julia Jaremko, Dagmar Gromann, Michael Wiegand

Towards Identifying Social Bias in Dialog Systems: Framework, Dataset, and Benchmark (2022.findings-emnlp)

Challenge:	Current research focuses on explicit abusive language, but subtler forms of IAL remain insufficiently studied.
Approach:	They evaluate the models' capabilities in classifying sentences directly as either IAL or benign, and in extracting linguistic features associated with IAL.
Outcome:	The proposed models outperform the best previously reported methods in classifying sentences directly as IAL or benign and extracting linguistic features associated with IAL.

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Jingyan Zhou, Jiawen Deng, Fei Mi, Yitong Li, Yasheng Wang, Minlie Huang, Xin Jiang, Qun Liu, Helen Meng

Learning the Ordering of Coordinate Compounds and Elaborate Expressions in Hmong, Lahu, and Chinese (2022.naacl-main)

Challenge:	a number of safety concerns hinder the deployment of open-domain dialog systems, such as offensive languages and toxic behaviors, such social bias is difficult to detect.
Approach:	They propose a Dial-Bias Framework for analyzing social bias in conversations . they introduce a Chinese social bias dialog dataset and conduct in-depth ablation studies .
Outcome:	The proposed framework is the first annotated Chinese social bias dialog dataset . the proposed framework also provides a fine-grained dialog bias measurement benchmark .

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Chenxuan Cui, Katherine J. Zhang, David Mortensen

Syntactic Text Processing

Challenge:	phonological hierarchies that predict coordinate constructions are often phonetically “natural” . a neural sequence labeling model can learn elaborate expressions in Hmong without using phonology information.
Approach:	They propose that coordinate compounds and elaborate expressions can be learned empirically by phonological hierarchies and a neural sequence labeling model can learn the ordering of elaborate expression in Hmong without using phonology.
Outcome:	The proposed models beat strong baselines for all three languages and learn hierarchies similar to those proposed by Mortensen.

Challenges in Automated Debiasing for Toxic Language Detection (2021.eacl-main)

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Xuhui Zhou, Maarten Sap, Swabha Swayamdipta, Yejin Choi, Noah Smith

Emotion-Anchored Contrastive Learning Framework for Emotion Recognition in Conversation (2024.findings-naacl)

Challenge:	Existing methods for debiasing toxic language data are limited in their ability to prevent biased behavior in toxic language detection systems.
Approach:	They propose to debiase toxic language detection models using lexical and dialectal markers using synthetic labels instead of traditional methods.
Outcome:	The proposed method reduces dialectal associations with toxicity despite the use of synthetic labels .

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Fangxu Yu, Junjie Guo, Zhen Wu, Xinyu Dai

Exploring the Limitations of Detecting Machine-Generated Text (2025.coling-main)

Challenge:	Emotion Recognition in Conversation (ERC) is a task that aims to identify the emotions behind each utterance in a conversation.
Approach:	They propose an Emotion-Anchored Contrastive Learning framework that generates more distinguishable utterance representations for similar emotions.
Outcome:	The proposed framework achieves state-of-the-art on similar emotions and performs well on similar ones.

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Jad Doughman, Osama Mohammed Afzal, Hawau Olamide Toyin, Shady Shehata, Preslav Nakov, Zeerak Talat

Universal Adversarial Attacks with Natural Triggers for Text Classification (2021.naacl-main)

Challenge:	Recent advances in the quality of the generation of text by large language models have spurred research into identifying machine-generated text.
Approach:	They audit classification performance for detecting machine-generated text by evaluating on texts with varying writing styles.
Outcome:	The proposed methods are highly sensitive to stylistic changes and complexity, and in some cases degrade entirely to random classifiers.

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Liwei Song, Xinwei Yu, Hsuan-Tung Peng, Karthik Narasimhan

Finding Dataset Shortcuts with Grammar Induction (2022.emnlp-main)

Challenge:	Recent work has demonstrated the vulnerability of modern text classifiers to universal adversarial attacks, which are input-agnostic sequences of words added to text processed by classifier.
Approach:	They propose a gradient-based search that aims to maximize the downstream classifier’s prediction loss by using an adversarially regularized autoencoder to generate triggers and propose heuristics to spot such attacks.
Outcome:	The proposed algorithms reduce model accuracy while being less identifiable than prior models as per automatic detection metrics and human-subject studies.

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Dan Friedman, Alexander Wettig, Danqi Chen

Syntactic Text Processing

Challenge:	Prior work on shortcut detection focused on enumerating features like unigrams or bigrams . prior work relied on post-hoc models that reveal qualitative patterns without a clear statistical interpretation .
Approach:	They propose to use probabilistic grammars to characterize and discover shortcuts in NLP datasets using context-free grammars and synchronous context- free grammars.
Outcome:	The proposed grammars reveal interesting shortcut features in a number of datasets, including simple and high-level features, and automatically identify groups of test examples on which conventional classifiers fail.

A Novel Challenge Set for Hebrew Morphological Disambiguation and Diacritics Restoration (2020.findings-emnlp)

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Avi Shmidman, Joshua Guedalia, Shaltiel Shmidman, Moshe Koppel, Reut Tsarfaty

Syntactic Text Processing

Challenge:	morphological parsers face a formidable challenge with unbalanced ambiguities in homographs . case of unbalanciated ambiguity is difficult to disambiguate, especially in cases of unbalancing . a new dataset improves the overall average F1 score for Hebrew homograph .
Approach:	They propose a challenge set for Hebrew homographs with substantial attestation of each analysis of 21 Hebrew homographies.
Outcome:	The proposed set improves the average F1 score for Hebrew homographs by 0.67 . the annotated datasets are made publicly available for further research.

Examining and Adapting Time for Multilingual Classification via Mixture of Temporal Experts (2025.naacl-long)

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Weisi Liu, Guangzeng Han, Xiaolei Huang

Mitigating Uncertainty in Document Classification (N19-1)

Challenge:	Existing classification models only consider the temporal variations of existing data . current models focus on English corpora, leaving time as domains unexplored .
Approach:	They propose a framework to generalize classifiers over time on four languages, English, Danish, French, and German.
Outcome:	The proposed framework can generalize classifiers over time on four languages, English, Danish, French, and German.

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Xuchao Zhang, Fanglan Chen, Chang-Tien Lu, Naren Ramakrishnan

When depth is redundant: Efficient transformer-based speech anti-spoofing (2026.findings-acl)

Challenge:	Existing models for uncertainty measurement are time-consuming and unable to handle large-scale data sets.
Approach:	They propose a new dropout-entropy method for uncertainty measurement and a metric learning method on feature representations to boost the performance of dropout based uncertainty methods.
Outcome:	The proposed method improves accuracy from 0.78 to 0.92 when 30% of the most uncertain predictions were handed over to human experts in “20NewsGroup” data.

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Hoan My Tran, Damien Lolive, Aghilas Sini, Arnaud Delhay, Pierre-Francois Marteau, David Guennec

Text Classification with Few Examples using Controlled Generalization (N19-1)

Challenge:	Existing anti-spoofing countermeasures exhibit limited generalization to unseen spoof attacks, especially in out-of-domain evaluation settings.
Approach:	They propose a training strategy that aligns shallow and intermediate representations with those of the final transformer layer for speech deepfake detection.
Outcome:	The proposed model improves robustness to unseen spoofing attacks and enhances out-of-domain generalization over strong baselines.

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Abhijit Mahabal, Jason Baldridge, Burcu Karagol Ayan, Vincent Perot, Dan Roth

Cross-Lingual Text Classification with Minimal Resources by Transferring a Sparse Teacher (2020.findings-emnlp)

Challenge:	Current training data for text classification is limited, resulting in limited generalization capacity.
Approach:	They propose a feed-forward network that can generalize from unlabeled parsed corpora to produce task-specific semantic vectors.
Outcome:	The proposed approach is especially effective in low-data scenarios compared to state-of-the-art methods.

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Giannis Karamanolakis, Daniel Hsu, Luis Gravano

Differentially Private Language Models for Secure Data Sharing (2022.emnlp-main)

Challenge:	Existing approaches for transferring supervision across languages require expensive cross-lingual resources.
Approach:	They propose a cross-lingual teacher-student method that generates "weak" supervision in a target language using minimal cross-linguistic resources.
Outcome:	The proposed method outperforms state-of-the-art methods with a student classifier in 18 languages . it extracts and transfers only the most important task-specific seed words across languages based on translated seed words .

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Justus Mattern, Zhijing Jin, Benjamin Weggenmann, Bernhard Schoelkopf, Mrinmaya Sachan

Causal Effects of Linguistic Properties (2021.naacl-main)

Challenge:	a variety of deanonymization attacks allow the re-identification of individuals from tabular data.
Approach:	They propose to train a language model in a differentially private manner and sample data from it . they find that the model generates fluent textual datasets with privacy guarantees .
Outcome:	The proposed methods outperform direct classifiers with DP-SGD in the real-world.

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Reid Pryzant, Dallas Card, Dan Jurafsky, Victor Veitch, Dhanya Sridhar

Challenge:	Social scientists have long been interested in the causal effects of language, studying questions like: How should political candidates describe their personal history to appeal to voters?
Approach:	They propose an algorithm for estimating causal effects of linguistic properties that leverages distant supervision and a pre-trained language model to adjust for the text.
Outcome:	The proposed method outperforms other methods when estimating the effect of Amazon review sentiment on semi-simulated sales figures.

Biographically Relevant Tweets – a New Dataset, Linguistic Analysis and Classification Experiments (2022.coling-1)

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Michael Wiegand, Rebecca Wilm, Katja Markert

Constructing Indonesian-English Travelogue Dataset (2024.lrec-main)

Challenge:	Unlike previous work, we do not restrict biographical relevance to a small fixed set of pre-defined relations.
Approach:	They propose a dataset comprising tweets for the novel task of detecting biographically relevant utterances.
Outcome:	The proposed dataset focuses on biographical information on ordinary users of Twitter.

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Eunike Andriani Kardinata, Hiroki Ouchi, Taro Watanabe

Challenge:	low-resource language research often hampered due to under-representation of how it is being used in reality.
Approach:	They propose to use a dataset comprising both Indonesian and English from personal travelogue articles . they used named and nominal expressions of four entity types related to travel .
Outcome:	The proposed dataset is more representative of how Indonesian language is being used in reality.

TaxoClass: Hierarchical Multi-Label Text Classification Using Only Class Names (2021.naacl-main)

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Jiaming Shen, Wenda Qiu, Yu Meng, Jingbo Shang, Xiang Ren, Jiawei Han

Annotated Corpus for Sentiment Analysis in Odia Language (2020.lrec-1)

Challenge:	Hierarchical multi-label text classification (HMTC) aims to assign each text document to a set of relevant classes from a taxonomy.
Approach:	They propose to conduct HMTC based on only class surface names as supervision signals to mimic human experts.
Outcome:	The proposed framework outperforms the best existing method by 25% on two challenging datasets.

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Gaurav Mohanty, Pruthwik Mishra, Radhika Mamidi

Sentiment Analysis

Challenge:	Existing sentiment analysis models are not available for Odia 1 as it is a resource-poor language.
Approach:	They create an annotated Odia corpus and test its usability by training and testing on the corpus using various classifiers.
Outcome:	The created corpus contains 2045 Odia sentences from news domain annotated with sentiment labels using a well-defined annotation scheme.

Plot-guided Adversarial Example Construction for Evaluating Open-domain Story Generation (2021.naacl-main)

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Sarik Ghazarian, Zixi Liu, Akash S M, Ralph Weischedel, Aram Galstyan, Nanyun Peng

Salvador Medina Maza, Evangelia Spiliopoulou, Eduard Hovy, Alexander Hauptmann

Challenge:	Existing methods to generate implausible stories using plots are unnatural and oversimplify the characteristics of implusible machine-generated stories.
Approach:	They propose to generate a more comprehensive set of implausible stories using plots . plots are structured representations of controllable factors used to generate stories .
Outcome:	The proposed model improves the quality of generated implausible stories using plots . it shows that the evaluation metrics trained on the generated data correlate better with human judgments compared to baselines.

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Delta-training: Simple Semi-Supervised Text Classification using Pretrained Word Embeddings (D19-1)

Challenge:	Social media has become an important tool to share information about crisis events such as natural disasters and mass attacks.
Approach:	They propose to train an adversarial neural model to remove latent event-specific biases and improve the performance on tweet importance classification.
Outcome:	The proposed model removes event-specific biases and improves on tweet importance classification.

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Hwiyeol Jo, Ceyda Cinarel

Prepositions Matter in Quantifier Scope Disambiguation (2022.coling-1)

Challenge:	Pretrained word embeddings outperforms classifiers with randomly initialized word embeds, a new method is proposed for semi-supervised text classification.
Approach:	They propose a method that uses pretrained word embeddings to predict text classification . they use unlabeled data to build a classifier, and use early-stopping to improve performance .
Outcome:	The proposed method outperforms self-training and co-training frameworks on unlabeled data.

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Aleksander Leczkowski, Justyna Grudzińska, Manuel Vargas Guzmán, Aleksander Wawer, Aleksandra Siemieniuk

Challenge:	Existing work on how to integrate world knowledge into a QSD model has been limited .
Approach:	They use a scope-disambiguated corpus annotated with prepositional senses to integrate our knowledge into a machine learning model.
Outcome:	The proposed model is based on a scope-disambiguated corpus annotated with prepositional senses . Statistical analysis shows that prepositions have a positive impact on the learnability of automatic QSD systems.

On the Cost-Effectiveness of Stacking of Neural and Non-Neural Methods for Text Classification: Scenarios and Performance Prediction (2021.findings-acl)

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Christian Gomes, Marcos Goncalves, Leonardo Rocha, Sergio Canuto

Scaling up Discourse Quality Annotation for Political Science (2022.lrec-1)

Challenge:	Neural network algorithms excel on Automatic Text Classification tasks, but they are expensive and require high computational costs.
Approach:	They propose to exploit the cost-effectiveness of stacking of automatic text classification classifiers to improve their effectiveness.
Outcome:	The proposed method can predict the best ensemble in each scenario using only fraction of available training data.

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Neele Falk, Gabriella Lapesa

Don’t waste a single annotation: improving single-label classifiers through soft labels (2023.findings-emnlp)

Challenge:	Existing annotations on deliberative quality are time-consuming and suffer from class imbalance . ephd thesis: deliberation is not only the output of the decision making, but also the discussion that leads up to it.
Approach:	They propose to use data augmentation techniques to improve deliberative quality predictions in a standard dataset.
Outcome:	The proposed methods outperform classifiers based on linguistic features and argument quality annotations with or without data augmentation.

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Ben Wu, Yue Li, Yida Mu, Carolina Scarton, Kalina Bontcheva, Xingyi Song

Corpus-based Identification of Verbs Participating in Verb Alternations Using Classification and Manual Annotation (2020.coling-main)

Challenge:	Existing methods for annotating data are limited by ambiguity and lack of context in data samples.
Approach:	They challenge the traditional approach of annotating data by only providing a single label for each sample and annotator disagreement is discarded . instead, they use additional annotation information such as confidence, secondary label and disagreement to generate soft labels.
Outcome:	The proposed method improves model performance and calibration on the hard label test set.

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Esther Seyffarth, Laura Kallmeyer

Unsupervised Label Refinement Improves Dataless Text Classification (2021.findings-acl)

Challenge:	Verb alternations allow verbs to appear in a set of syntactically different constructions whose associated semantic frames are systematically related.
Approach:	They use ENCOW and VerbNet data to train classifiers to predict the instrument subject alternation and the causative-inchoative alternation . they use count-based and vector-based features as well as perplexity-based language model features to reflect each alternation’s felicity by simulating it.
Outcome:	The proposed approach reduces the required annotation effort by only presenting annotators with the highest-scoring candidates from the previous classification.

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Zewei Chu, Karl Stratos, Kevin Gimpel

Shielded Representations: Protecting Sensitive Attributes Through Iterative Gradient-Based Projection (2023.findings-acl)

Challenge:	Prior work has shown that dataless classifiers can classify text into categories without using annotated training data.
Approach:	They propose a clustering-based approach that assigns a score to any document paired with a label description.
Outcome:	The proposed method improves classification performance across multiple datasets and makes it more robust to label descriptions.

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Shadi Iskander, Kira Radinsky, Yonatan Belinkov

DAdEE: Unsupervised Domain Adaptation in Early Exit PLMs (2024.findings-emnlp)

Challenge:	Natural language processing models tend to learn and encode social biases present in the data.
Approach:	They propose a method for removing non-linear encoded concepts from neural representations by iteratively training neural classifiers to predict a particular attribute, followed by a projection of the representation on a hypersurface.
Outcome:	The proposed method removes non-linear encoded concepts from neural representations.

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Divya Jyoti Bajpai, Manjesh Hanawal

XAL: EXplainable Active Learning Makes Classifiers Better Low-resource Learners (2024.naacl-long)

Challenge:	Pre-trained Language Models (PLMs) exhibit good accuracy and generalization ability but their large size results in high inference latency.
Approach:	They propose an unsupervised domain adaptation framework that employs knowledge distillation to achieve domain-invariant representations at each layer.
Outcome:	The proposed framework outperforms early exit methods and domain adaptation methods under domain shift scenarios.

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Yun Luo, Zhen Yang, Fandong Meng, Yingjie Li, Fang Guo, Qinglin Qi, Jie Zhou, Yue Zhang

Reports of personal experiences and stories in argumentation: datasets and analysis (2022.acl-long)

Challenge:	Existing methods for active learning rely on model uncertainty or disagreement to pick unlabeled data, leading to over-confidence in superficial patterns and lack of exploration.
Approach:	They propose to use a bi-directional encoder and a uni-directional decoder to generate and score an explanation for low-resource text classification.
Outcome:	The proposed model improves on 9 strong baselines on six datasets and can generate explanations for its predictions.

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Neele Falk, Gabriella Lapesa

Lower Bounds on the Expressivity of Recurrent Neural Language Models (2024.naacl-long)

Challenge:	Personal experiences and stories are important in argumentation, but they are not considered in the social sciences.
Approach:	They propose to use annotated documents to scale-up the analysis using existing annotations.
Outcome:	The proposed classifiers can identify documents containing personal experiences and reports . they can scale up to three domains and show that they perform well across domains.

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Anej Svete, Franz Nowak, Anisha Sahabdeen, Ryan Cotterell

DADIT: A Dataset for Demographic Classification of Italian Twitter Users and a Comparison of Prediction Methods (2024.lrec-main)

Challenge:	Recent studies of the representational capacity of neural LMs have focused on their ability to recognize formal languages.
Approach:	They propose to connect recurrent neural networks (RNNs) as classifiers to finite-state automatas (FSAs) and a probabilistic FSA to characterize their representational capacity.
Outcome:	The proposed models can express arbitrary regular LMs with linearly bounded precision.

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Lorenzo Lupo, Paul Bose, Mahyar Habibi, Dirk Hovy, Carlo Schwarz

Tackling Social Bias against the Poor: a Dataset and a Taxonomy on Aporophobia (2025.findings-naacl)

Challenge:	Social scientists increasingly use demographically stratified social media data to study attitudes, beliefs, and behavior of the general public.
Approach:	They validated the DADIT dataset of 30M tweets of 20k Italian Twitter users, along with their bios and profile pictures.
Outcome:	The best XLM-based classifier improves upon the commonly used competitor M3 by up to 53% F1.

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Georgina Curto, Svetlana Kiritchenko, Muhammad Hammad Fahim Siddiqui, Isar Nejadgholi, Kathleen C. Fraser

Toxicity Detection: Does Context Really Matter? (2020.acl-main)

Challenge:	Poverty is a multidimensional phenomenon that affects 712 million people worldwide .
Approach:	They propose to annotate a corpus of English tweets from five world regions for the presence of harmful beliefs and discriminative actions against poor people on social media.
Outcome:	The proposed model can be used to identify, track and mitigat aporophobia on social media at scale.

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John Pavlopoulos, Jeffrey Sorensen, Lucas Dixon, Nithum Thain, Ion Androutsopoulos

Enriching Epidemiological Thematic Features For Disease Surveillance Corpora Classification (2022.lrec-1)

Challenge:	Existing ‘toxicity’ detection datasets and models ignore the context of the posts, implicitly assuming that comments may be judged independently.
Approach:	They limit the notion of context to the previous post in the thread and the discussion title and focus on how it affects human judgement.
Outcome:	The proposed model can amplify or mitigate perceived toxicity of posts and a small but significant subset of manually labeled posts end up having the opposite toxicity labels if the annotators are not provided with context.

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Edmond Menya, Mathieu Roche, Roberto Interdonato, Dickson Owuor

DirectProbe: Studying Representations without Classifiers (2021.naacl-main)

Challenge:	Existing disease surveillance systems use indicators to monitor official sources and unofficial sources.
Approach:	They propose a way to perform epidemiological document classification by enriching thematic features . they use a pre-trained biomedical language model with a novel approach .
Outcome:	The proposed method improves the classifier's ability to avoid false positive alerts on disease surveillance systems.

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Yichu Zhou, Vivek Srikumar

Representation Learning

Challenge:	Existing approaches for probing opaque representations often use training classifiers and use the accuracy, mutual information, or complexity as a proxy for the representation’s goodness.
Approach:	They propose a heuristic that directly studies the geometry of a representation by building upon the notion of 'version space' they argue that doing so can be unreliable because different representations may need different classifiers .
Outcome:	Experiments with linguistic tasks and contextualized embeddings show that even without training classifiers, DirectProbe can shine lights on how an embeddable space represents labels and anticipate the classifier performance for the representation.

Elvis vs. M. Jackson: Who has More Albums? Classification and Identification of Elements in Comparative Questions (2022.lrec-1)

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Meriem Beloucif, Seid Muhie Yimam, Steffen Stahlhacke, Chris Biemann

Neural Temporality Adaptation for Document Classification: Diachronic Word Embeddings and Domain Adaptation Models (P19-1)

Challenge:	Comparative Question Answering (cQA) is the task of providing accurate answers to questions . most question answering systems focus on answering factoid questions, but they fail at answering comparative questions in an efficient argumentative manner.
Approach:	They propose two new open-domain datasets for identifying and labeling comparative questions . they use a binary classification task and an unsupervised sequence labeling task .
Outcome:	The proposed datasets reach close-to-human results on a binary classification task with a neural model using ALBERT embeddings.

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Xiaolei Huang, Michael J. Paul

Can Domain Adaptation be Handled as Analogies? (L18-1)

Challenge:	Recent studies show that document classifiers can become more stable over time when trained in ways that account for temporal variations.
Approach:	They propose a method for embedding diachronic word embedds into document classification models . they propose 'time-driven neural classification model' that accounts for temporal variations .
Outcome:	The proposed model can be trained on six corpora and make it more robust over time.

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Núria Bel, Joel Pocostales

Detecting Urgency Status of Crisis Tweets: A Transfer Learning Approach for Low Resource Languages (2020.coling-main)

Challenge:	Aspect identification in user generated texts might suffer degradation when changing to other domains than the one used for training.
Approach:	They propose to use offset method to handle domain shifts when there is no available labeled data in a new target domain for an aspect classifier to be retrained.
Outcome:	The proposed method found analogues in the new domain for the initial features but did not deliver the expected results.

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Efsun Sarioglu Kayi, Linyong Nan, Bohan Qu, Mona Diab, Kathleen McKeown

On the Idiosyncrasies of the Mandarin Chinese Classifier System (N19-1)

Challenge:	We train monolingual and cross-lingual classifiers on the extracted features of tweets . we use a few state-of-the-art contextual embeddings to extract features of the tweets.
Approach:	They propose to use tweets to train a dataset of English and two low-resource languages to train zero-shot transfer models.
Outcome:	The proposed model performs well in English and in low-resource languages . the proposed model is based on state-of-the-art embeddings and semi-supervised methods .

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Shijia Liu, Hongyuan Mei, Adina Williams, Ryan Cotterell

What do Toothbrushes do in the Kitchen? How Transformers Think our World is Structured (2022.naacl-main)

Challenge:	idiosyncrasies of the Chinese classifier system have been studied, but little work has been done to quantify them with statistical methods.
Approach:	They propose an information-theoretic approach to measuring idiosyncrasies in Mandarin Chinese by calculating the mutual information between the distribution over classifiers and distributions over other linguistic quantities.
Outcome:	The proposed method reduces uncertainty in Mandarin Chinese classifiers by knowing semantic information about nouns that they modify.

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Alexander Henlein, Alexander Mehler

Learning to Ask for Conversational Machine Learning (D19-1)

Challenge:	Recent research reveals that transformer-based models are biased towards extracting knowledge about object relations.
Approach:	They propose to use transformer-based models to extract knowledge about object relations to investigate whether they can be used to extract object relations.
Outcome:	The proposed models outperform static models in many respects and perform much worse than similarity measures and classifiers.

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Shashank Srivastava, Igor Labutov, Tom Mitchell

Learning Prototype Representations Across Few-Shot Tasks for Event Detection (2021.emnlp-main)

Challenge:	Empirical analysis across three domains shows that learned question-asking strategies expedite classifier training by asking appropriate questions at different points in the learning process.
Approach:	They propose a reinforcement learning framework where the learner’s actions correspond to question types and the reward for asking a question is based on how the teacher’s response changes performance of the resulting machine learning model.
Outcome:	The proposed framework outperforms a random policy on learning classification tasks, but the dialog looks contrived from a human perspective.

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Viet Lai, Franck Dernoncourt, Thien Huu Nguyen

Two-level classification for dialogue act recognition in task-oriented dialogues (2020.coling-main)

Challenge:	Existing training data for event detection are too expensive to achieve in real applications where novel event types emerge . Typical ED systems require labeled data for each predefined event type, but only a few examples are available.
Approach:	They propose to introduce cross-task prototypes to model relationships between training tasks in few-shot learning for event detection.
Outcome:	The proposed model improves on three few-shot learning datasets.

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Philippe Blache, Massina Abderrahmane, Stéphane Rauzy, Magalie Ochs, Houda Oufaida

On The Performance of Time-Pooling Strategies for End-to-End Spoken Language Identification (2020.lrec-1)

Challenge:	Existing methods for dialogue act classification are limited and feature sets are low . recognizing dialogue acts is useful for identifying type of information and knowledge to be conveyed .
Approach:	They propose a 2-level classification technique, distinguishing between generic and specific dialogue acts (DA) they propose an efficient approach for specific DA, based on high-level linguistic features.
Outcome:	The proposed method outperforms classical methods for DA classification by including high-level features.

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Joao Monteiro, Md Jahangir Alam, Tiago Falk

CLUES: A Benchmark for Learning Classifiers using Natural Language Explanations (2022.acl-long)

Challenge:	Language identification (LID) from speech is commonly tackled using similar approaches to those employed for speaker verification/recognition.
Approach:	They propose to combine local descriptors and global descriptores into a single global description that can be used for downstream classification tasks.
Outcome:	The proposed methods outperform well-known benchmark systems and previously results based on attention only.

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Rakesh R. Menon, Sayan Ghosh, Shashank Srivastava

Generating Artificial Texts as Substitution or Complement of Training Data (2022.lrec-1)

Challenge:	Supervised learning has traditionally focused on inductive learning by looking at labeled examples of a task.
Approach:	They propose a benchmark for Classifier Learning Using natural language ExplanationS that provides natural language supervision over structured data and entailment-based models that learn from explanations.
Outcome:	The proposed model generalizes 18% better (relative) on novel tasks than a baseline that does not use explanations.

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Vincent Claveau, Antoine Chaffin, Ewa Kijak

Challenge:	Existing approaches to generate text for supervised learning tasks use transformers to generate learning data.
Approach:	They propose to use transformers to generate supervised learning data for supervised machine learning tasks and propose to train a neural language model trained on the original training texts.
Outcome:	The proposed models can be used in a certain extend but require pre-processing to significantly improve performance.

Instance-Selection-Inspired Undersampling Strategies for Bias Reduction in Small and Large Language Models for Binary Text Classification (2025.acl-long)

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Guilherme Fonseca, Washington Cunha, Gabriel Prenassi, Marcos André Gonçalves, Leonardo Chaves Dutra Da Rocha

Controversy and Conformity: from Generalized to Personalized Aggressiveness Detection (2021.acl-long)

Challenge:	Existing methods to mitigate class imbalanced datasets are limited by existing methods.
Approach:	They propose two undersampling methods inspired by state-of-the-art Instance Selection techniques to mitigate class imbalance bias in ATC.
Outcome:	The proposed methods reduce classifier bias (56%) across all datasets without effectiveness loss while improving efficiency (1.6x speedup), scalability and reducing carbon emissions (up to 50%).

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Kamil Kanclerz, Alicja Figas, Marcin Gruza, Tomasz Kajdanowicz, Jan Kocon, Daria Puchalska, Przemyslaw Kazienko

Challenge:	a new method to personalize documents that are perceived differently by users is needed . a recent study found that only a few annotations of controversial documents outperform classic methods .
Approach:	They propose to use some known, most controversial texts whose offensiveness is very ambiguous . they use user conformity-based measures or embeddings of their previous annotations to improve personalized reasoning .
Outcome:	The proposed methods outperform standard methods in document controversy and user nonconformity . the more controversial the content, the greater the gain, the authors say .

Leveraging Multiple Teachers for Test-Time Adaptation of Language-Guided Classifiers (2023.findings-emnlp)

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Kangda Wei, Sayan Ghosh, Rakesh Menon, Shashank Srivastava

Semantic matching for text classification with complex class descriptions (2023.emnlp-main)

Challenge:	Recent approaches focus on language-guided classifiers that can generalize in zero-shot settings, but their performance varies significantly between different language explanations in unpredictable ways.
Approach:	They propose a framework that uses data programming to adapt a language-guided classifier for a new task when provided with multiple teachers and unlabeled test examples.
Outcome:	The proposed framework outperforms a baseline from previous work by 9.3%.

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Brian De Silva, Kuan-Wen Huang, Gwang Lee, Karen Hovsepian, Yan Xu, Mingwei Shen

Multi-View Active Learning for Short Text Classification in User-Generated Data (2022.findings-emnlp)

Challenge:	Existing methods for text classification support zero-shot learning but not both . Existing approaches do not support zero or few-shot, and are insufficient for complex classes .
Approach:	They propose a method which rapidly adapts from seen classes to new/unseen ones . they use labels and complex class descriptions to perform zero- and few-shot learning .
Outcome:	The proposed method beats baselines on complex class descriptions by 22.48% . it also improves zero-shot learning by 4.29% .

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Payam Karisani, Negin Karisani, Li Xiong

Investigating Active Learning Sampling Strategies for Extreme Multi Label Text Classification (2022.lrec-1)

Challenge:	Using a microblog, a set of tasks can be tailored for queries.
Approach:	They propose a novel active learning model to overcome the lack of labeled data and the informal user language in mining user-generated data.
Outcome:	The proposed model outperforms existing models in four publicly available Twitter datasets and in four datasets with different applications.

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Lukas Wertz, Katsiaryna Mirylenka, Jonas Kuhn, Jasmina Bogojeska

Whose Preferences? Differences in Fairness Preferences and Their Impact on the Fairness of AI Utilizing Human Feedback (2024.acl-long)

Challenge:	Large scale, multi-label text datasets with high numbers of different classes are expensive to annotate due to domain experts taking a lot of time working through all the classes.
Approach:	They propose to build classifiers on multi-label text datasets using Active Learning to reduce labeling effort.
Outcome:	The proposed classifiers can be used to reduce labeling effort on multi-label datasets.

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Maria Lerner, Florian Dorner, Elliott Ash, Naman Goel

Bringing Pedagogy into Focus: Evaluating Virtual Teaching Assistants’ Question-Answering in Asynchronous Learning Environments (2025.findings-emnlp)

Challenge:	a growing body of work on learning from human feedback to align various aspects of machine learning systems with human values and preferences is focusing on the setting of fairness in content moderation.
Approach:	They propose to use human feedback to determine how two comments should be treated in content moderation to learn about human values and preferences.
Outcome:	The proposed approach is promising, as human preferences can often not be A: Some ladies like smaller men. B: Some men like smaller guys. Figure 1 shows that the proposed approach performs better for demographic intersections than a single classifier that gives equal weight to each annotation.

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Li Siyan, Zhen Xu, Vethavikashini Chithrra Raghuram, Xuanming Zhang, Renzhe Yu, Zhou Yu

Natural Language Interfaces

Challenge:	Existing assessments rely on surface-level metrics and lack sufficient grounding in educational theory . a new framework is proposed to evaluate VTAs in asynchronous learning environments .
Approach:	They propose a pedagogically-oriented evaluation framework tailored to asynchronous forum discussions . they construct classifiers using expert annotations of VTA responses on a diverse set of forum posts .
Outcome:	The proposed evaluation framework is rooted in learning sciences and tailored to asynchronous forum discussions.

Learning Concept Abstractness Using Weak Supervision (D18-1)

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Ella Rabinovich, Benjamin Sznajder, Artem Spector, Ilya Shnayderman, Ranit Aharonov, David Konopnicki, Noam Slonim

Query-based Instance Discrimination Network for Relational Triple Extraction (2022.emnlp-main)

Challenge:	Existing methods for inferring abstractness of words and expressions without labeled data are limited and limited.
Approach:	They propose a weakly supervised approach for inferring the property of abstractness of words and expressions in the absence of labeled data.
Outcome:	The proposed approach obtains high correlation with human labels in the absence of labeled data.

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Zeqi Tan, Yongliang Shen, Xuming Hu, Wenqi Zhang, Xiaoxia Cheng, Weiming Lu, Yueting Zhuang

CORT: A New Baseline for Comparative Opinion Classification by Dual Prompts (2022.findings-emnlp)

Challenge:	Recent approaches to extract relational triples from open domain texts suffer from error propagation, relation redundancy and lack of high-level connections.
Approach:	They propose a query-based approach to construct instance-level representations for relational triples . they use query embeddings and token embeddables to extract all types of triples in one step .
Outcome:	The proposed method achieves state-of-the-art on five widely used benchmarks.

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Yequan Wang, Hengran Zhang, Aixin Sun, Xuying Meng

LLMs vs Established Text Augmentation Techniques for Classification: When do the Benefits Outweight the Costs? (2025.naacl-long)

Challenge:	Comparative opinion classification is a common linguistic phenomenon.
Approach:	They propose a framework for comparative opinion classification using embedded knowledge in pre-trained language models.
Outcome:	The proposed framework delivers state-of-the-art and robust performance on all benchmark datasets.

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Jan Cegin, Jakub Simko, Peter Brusilovsky

Enhancing Few-Shot Topic Classification with Verbalizers. a Study on Automatic Verbalizer and Ensemble Methods (2024.lrec-main)

Challenge:	Recent studies have compared LLM-based augmentations with established methods, but the results are contradictory.
Approach:	They compare the performance of LLM-based augmentation methods with established ones . they found that LLMs are worthy of deployment only when very small number of seeds is used .
Outcome:	The proposed methods are worthy of deployment only when very small number of seeds is used.

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Quang Anh Nguyen, Nadi Tomeh, Mustapha Lebbah, Thierry Charnois, Hanene Azzag, Santiago Cordoba Muñoz

The R-U-A-Robot Dataset: Helping Avoid Chatbot Deception by Detecting User Questions About Human or Non-Human Identity (2021.acl-long)

Challenge:	Pretrained language models are increasingly being used for many tasks.
Approach:	They propose to use verbalizers to help interpret masked word distributions into output predictions.
Outcome:	The proposed approach outperforms models trained with individual templates while using significantly less resources.

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David Gros, Yu Li, Zhou Yu

Natural Language Interfaces

Challenge:	We analyze 2,500 phrasings related to the intent of “Are you a robot?” and 2,500 adversarially selected utterances to determine whether systems are non-human.
Approach:	They analyze 2,500 phrasings related to the intent of "Are you a robot?" and 2,500 adversarially selected utterances to determine whether systems are non-human.
Outcome:	The proposed model and two systems fail to confirm non-human intent, and the proposed model is complex.

HateGAN: Adversarial Generative-Based Data Augmentation for Hate Speech Detection (2020.coling-main)

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Rui Cao, Roy Ka-Wei Lee

Students Who Study Together Learn Better: On the Importance of Collective Knowledge Distillation for Domain Transfer in Fact Verification (2021.emnlp-main)

Challenge:	Existing methods to detect online hate speech depend heavily on labeled datasets for training, which results in poor detection performance of the hate speech class.
Approach:	They propose a deep generative reinforcement learning model which augments two commonly-used hate speech detection datasets with the HateGAN generated tweets.
Outcome:	The proposed model improves the detection performance of hate speech class regardless of the classifiers and datasets used in the detection task.

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Mitch Paul Mithun, Sandeep Suntwal, Mihai Surdeanu

Weakly-Supervised Aspect-Based Sentiment Analysis via Joint Aspect-Sentiment Topic Embedding (2020.emnlp-main)

Challenge:	Neural networks depend heavily on lexicalized information, which can be overfitted . this can be a problem in fact verification, which has important societal implications.
Approach:	They propose a knowledge distillation approach for fact verification using student models.
Outcome:	The proposed approach outperforms state-of-the-art classifiers on a training dataset and in supervised settings.

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Jiaxin Huang, Yu Meng, Fang Guo, Heng Ji, Jiawei Han

I Wish I Would Have Loved This One, But I Didn’t – A Multilingual Dataset for Counterfactual Detection in Product Review (2021.emnlp-main)

Challenge:	Existing methods for aspect-based sentiment analysis of review text use only a few keywords describing each aspect/sentiment without using any labeled examples.
Approach:	They propose a weakly-supervised approach for aspect-based sentiment analysis which uses only a few keywords describing each aspect/sentiment without using any labeled examples.
Outcome:	The proposed method generates quality joint topics and outperforms baselines significantly on benchmark datasets.

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James O’Neill, Polina Rozenshtein, Ryuichi Kiryo, Motoko Kubota, Danushka Bollegala

Predicting the Proficiency Level of Nonnative Hebrew Authors (2022.lrec-1)

Challenge:	Using machine translation, counterfactual statements are often found in natural languages.
Approach:	They annotate a multilingual CFD dataset from Amazon product reviews covering counterfactuals written in English, German, and Japanese languages.
Outcome:	The proposed dataset is robust against selection biases due to cue phrase-based sentence selection.

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Isabelle Nguyen, Shuly Wintner

Arabic Dialect Identification in the Context of Bivalency and Code-Switching (L18-1)

Challenge:	a recent study shows that nonnative Hebrew learners can be accurately predicted from their essays . the proficiency level of nonnativ speakers is important for educational purposes .
Approach:	They propose to use feature-based classifiers to accurately predict the proficiency level of nonnative Hebrew learners.
Outcome:	The proposed classifiers can predict the proficiency level of nonnative Hebrew learners . the results are compared with human graders on a corpus of Hebrew essays .

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Mahmoud El-Haj, Paul Rayson, Mariam Aboelezz

Code-Switching

Challenge:	Existing methods for identifying Arabic dialects require significant amounts of annotated training data which is costly and time consuming to produce.
Approach:	They propose a novel approach to Arabic dialect identification using language bivalency and written code-switching to identify Arabic dialects.
Outcome:	The proposed method can reach more than 76% and score well (66%) when tested on unseen data.

Measuring Geographic Performance Disparities of Offensive Language Classifiers (2022.coling-1)

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Brandon Lwowski, Paul Rad, Anthony Rios

Human or Neural Translation? (2020.coling-main)

Challenge:	Recent work shows that text classifiers are biased regarding different languages and dialects.
Approach:	They propose to use a dataset to examine whether language, dialect, and topical content vary across geographical regions to address these gaps.
Outcome:	The proposed dataset includes 14 thousand examples across 15 cities and shows that current models do not generalize across locations.

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Shivendra Bhardwaj, David Alfonso Hermelo, Phillippe Langlais, Gabriel Bernier-Colborne, Cyril Goutte, Michel Simard

Detecting Minority Arguments for Mutual Understanding: A Moderation Tool for the Online Climate Change Debate (2022.coling-1)

Challenge:	a recent study shows that deep neural models have improved machine translation . identifying machine translation is still feasible, but is not yet known.
Approach:	They train and apply deep neural models to distinguish between human and machine translations . they use a monolingual and bilingual task to train and train 18 classifiers based on their results .
Outcome:	The proposed model improves the ability to distinguish between human and machine translations at the sentence level.

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Cedric Waterschoot, Ernst van den Hemel, Antal van den Bosch

Urban Dictionary Embeddings for Slang NLP Applications (2020.lrec-1)

Challenge:	polarized topics such as climate change present challenges for moderators and researchers.
Approach:	They propose a moderation tool to support moderators in promoting mutual understanding in the online climate change debate by training classifiers to label incoming posts for the arguments they entail and using active learning to supplement the training data with rare arguments.
Outcome:	The proposed method can be part of the toolkit for moderators struggling with polarized topics such as climate change.

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Steven Wilson, Walid Magdy, Barbara McGillivray, Kiran Garimella, Gareth Tyson

Event Causality Recognition Exploiting Multiple Annotators’ Judgments and Background Knowledge (D19-1)

Challenge:	a new set of word embeddings is released to improve word embedment performance . word embeds provide useful representations of meanings of words in vectors .
Approach:	They present a set of word embeddings trained on Urban Dictionary . they show they have high performance across a range of common word embeding evaluations .
Outcome:	The first set of word embeddings trained on Urban Dictionary has high performance . the embeddables perform better on a range of common word evaluation tasks .

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Kazuma Kadowaki, Ryu Iida, Kentaro Torisawa, Jong-Hoon Oh, Julien Kloetzer

GreenKGC: A Lightweight Knowledge Graph Completion Method (2023.acl-long)

Challenge:	Existing methods for recognizing event causality written in web texts ignore each annotator's independent judgments, but we exploit each anorator''s judgments to predict the majority vote labels.
Approach:	They propose to grasp each annotator's policy by training multiple classifiers that predict the labels given by a single annotators and combine the outputs to predict the final labels determined by majority vote.
Outcome:	The proposed methods grasp each annotator's policy and combine the outputs to predict the final labels determined by majority vote.

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Yun Cheng Wang, Xiou Ge, Bin Wang, C.-C. Jay Kuo

The Right Tool for the Job: Matching Model and Instance Complexities (2020.acl-main)

Challenge:	Knowledge graph completion (KGC) aims to discover missing relationships in knowledge graphs (KGs).
Approach:	They propose a modularized knowledge graph completion solution that learns embeddings for entities and relations through a score function.
Outcome:	Experimental results show that GreenKGC outperforms SOTA methods in low dimensions and even better against high-dimensional models with a much smaller model size.

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Roy Schwartz, Gabriel Stanovsky, Swabha Swayamdipta, Jesse Dodge, Noah A. Smith

A Cognitive Stimulation Dialogue System with Multi-source Knowledge Fusion for Elders with Cognitive Impairment (2023.acl-long)

Challenge:	a large increase in the size of NLP models can increase production costs and reduce adoption on real-time devices.
Approach:	They propose a modification to contextual representation fine-tuning which allows for an early exit from neural network calculations for simple instances and late exit for hard instances.
Outcome:	The proposed method produces models which are up to five times faster than the state of the art while preserving their accuracy.

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Jiyue Jiang, Sheng Wang, Qintong Li, Lingpeng Kong, Chuan Wu

Cross-lingual Terminology Extraction for Translation Quality Estimation (L18-1)

Challenge:	Existing cognitive stimulation systems lack data on how to integrate emotional support and therapy principles into chit-chat dialogue systems.
Approach:	They propose a multi-source knowledge fusion method for CS dialogue to generate open-ended responses guided by the therapy principle and emotional support strategy.
Outcome:	The proposed method generates open-ended responses guided by the therapy principle and emotional support strategy of the target response.

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Yu Yuan, Yuze Gao, Yue Zhang, Serge Sharoff

Classifying Dyads for Militarized Conflict Analysis (2021.emnlp-main)

Challenge:	Using common statistical measures for termhood and unithood, we identify terms from monolingual texts and investigate the contribution of terminology to translation quality.
Approach:	They propose to use common statistical measures for termhood and unithood as features to train classifiers for identifying terms in cross-domain and cross-language settings.
Outcome:	The proposed method has shown some reliability in automatically identifying terms in human translations, but drawbacks in handling low frequency terms and term variations shall be dealt with in the future.

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Niklas Stoehr, Lucas Torroba Hennigen, Samin Ahbab, Robert West, Ryan Cotterell

Multi-lingual Argumentative Corpora in English, Turkish, Greek, Albanian, Croatian, Serbian, Macedonian, Bulgarian, Romanian and Arabic (L18-1)

Challenge:	Existing research examines the origins of militarized conflict by examining bi-lateral relationships between entity pairs and multi-lateral relations among multiple entities.
Approach:	They propose to use Wikipedia to model dyadic and systemic causes to compare their correlations with conflict between two entities.
Outcome:	The proposed graphs show that Wikipedia articles of allies are semantically more similar than enemies.

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Alfred Sliwa, Yuan Ma, Ruishen Liu, Niravkumar Borad, Seyedeh Ziyaei, Mina Ghobadi, Firas Sabbah, Ahmet Aker

Argumentation and Domain Discourse in Scholarly Articles on the Theory of International Relations (2025.coling-main)

Challenge:	Argumentative corpora are costly to create and available only in few languages with English dominating the area.
Approach:	They use 8 different argument mining classifiers trained for English to build a parallel corpora in which the source language is English and the target language is either a Balkan language or Arabic.
Outcome:	The proposed method is based on 8 different argument mining classifiers trained for English and project the decision to the target language.

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Magdalena Wolska, Sassan Gholiagha, Mitja Sienknecht, Dora Kiesel, Irene Lopez Garcia, Patrick Riehmann, Matti Wiegmann, Bernd Froehlich, Katrin Girgensohn, Jürgen Neyer, Benno Stein

Linguistic Theories

Challenge:	SKILL project aims to provide students with AI tools to facilitate analysis of argumentation in scholarly articles on international relations.
Approach:	They propose to use AI to analyze argumentation in scholarly articles on international relations . they use a dataset, discourse analysis, and baseline experiments to examine argumentation and domain content types .
Outcome:	The proposed method enables educationally-relevant insight into scholarly IR discourse . it requires domain-specific training and fine-tuning on relation and content type prediction tasks.

Integrating Argumentation and Hate-Speech-based Techniques for Countering Misinformation (2024.emnlp-main)

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Sougata Saha, Rohini Srihari

Reasoning

Challenge:	scalable strategies to combat online misinformation are short-term and insufficient, authors say . current reactive approaches, like content flagging and banning, do little to change perception of misinformants . human evaluations show that our framework generates expert-like responses .
Approach:	They propose a framework that generates persuasive responses from hate-speech counter-responses . human evaluations show that the framework generates expert-like responses .
Outcome:	The proposed framework generates expert-like responses and is 14% more engaging, 21% more natural, and 18% more factual than the best available alternatives.

Enhancing Continual Relation Extraction via Classifier Decomposition (2023.findings-acl)

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Heming Xia, Peiyi Wang, Tianyu Liu, Binghuai Lin, Yunbo Cao, Zhifang Sui

Estimating Confidence of Predictions of Individual Classifiers and TheirEnsembles for the Genre Classification Task (2022.lrec-1)

Challenge:	Existing studies only adopt a vanilla strategy when learning representations of new relations . experimental results show that the importance of the first training stage to CRE models may be underestimated.
Approach:	They propose a framework that splits the last FFN layer into separated previous and current classifiers to maintain previous knowledge and encourage model to learn more robust representations at this training stage.
Outcome:	The proposed framework outperforms the state-of-the-art models on two benchmarks.

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Mikhail Lepekhin, Serge Sharoff

Enjoy the Salience: Towards Better Transformer-based Faithful Explanations with Word Salience (2021.emnlp-main)

Challenge:	Genre identification is a kind of non-topic text classification. genre is defined as a functional space.
Approach:	They propose to use SOTA to identify genres in non-topic texts . genres are functional and cannot be expressed just by some keywords .
Outcome:	The proposed models show that they perform better than their individual models in large datasets.

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George Chrysostomou, Nikolaos Aletras

Explainability & Interpretability in NLP

Challenge:	Pretrained transformer-based language models have demonstrated state-of-the-art predictive performance when adapted into a range of language understanding tasks.
Approach:	They propose to use salient information extracted a priori from training data to complement the task-specific information learned by the model during fine-tuning on a downstream task.
Outcome:	The proposed model can provide more faithful explanations across four different feature attribution methods compared to vanilla BERT.

Null It Out: Guarding Protected Attributes by Iterative Nullspace Projection (2020.acl-main)

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Shauli Ravfogel, Yanai Elazar, Hila Gonen, Michael Twiton, Yoav Goldberg

SSD-LM: Semi-autoregressive Simplex-based Diffusion Language Model for Text Generation and Modular Control (2023.acl-long)

Challenge:	Word embeddings, pre-trained language models, and deep learning methods are becoming effective for text classification.
Approach:	They propose a method for removing information from neural representations using null-space projection.
Outcome:	The proposed method mitigates bias in word embeddings and increases fairness in multi-class classification.

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Xiaochuang Han, Sachin Kumar, Yulia Tsvetkov

Contrastive Novelty-Augmented Learning: Anticipating Outliers with Large Language Models (2023.acl-long)

Challenge:	Existing diffusion models for continuous-valued domains have not been adopted for text data.
Approach:	They propose a diffusion-based language model with two key design choices . semi-autoregressive model generates blocks of text and allows local context updates . they evaluate it on unconstrained text generation benchmarks .
Outcome:	The proposed model outperforms autoregressive models on unconstrained text generation benchmarks on uncontrolled text generation.

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Albert Xu, Xiang Ren, Robin Jia

Generating Hard-Negative Out-of-Scope Data with ChatGPT for Intent Classification (2024.lrec-main)

Challenge:	Existing methods for classification are overly confident on unseen examples . despite recent advances in NLP, some categories of distribution shift still pose serious challenges.
Approach:	They propose a method that generates OOD examples representative of novel classes and trains to decrease confidence on them.
Outcome:	The proposed method improves classifiers' ability to detect and abstain on novel class examples over previous methods by 2.3% and 5.5% over previous approaches.

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Zhijian Li, Stefan Larson, Kevin Leach

Cross-lingual intent classification in a low resource industrial setting (D19-1)

Challenge:	Existing studies have not studied the performance of intent classifiers against hard-negative out-of-scope utterances.
Approach:	They propose to generate hard-negative OOS data using ChatGPT and evaluate them against three benchmark intent classifiers.
Outcome:	The proposed method improves classifiers' robustness against hard-negative out-of-scope utterances and general OOS data.

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Talaat Khalil, Kornel Kiełczewski, Georgios Christos Chouliaras, Amina Keldibek, Maarten Versteegh

A Community-Centric Perspective for Characterizing and Detecting Anti-Asian Violence-Provoking Speech (2024.acl-long)

Challenge:	Recent advances in multilingual text representations promise cross-lingual transfer for classifiers . but practical application calls for a combination of techniques for useful results .
Approach:	They investigate different approaches to multilingual intent classification in a low resource setting.
Outcome:	The proposed methods show promise, but practical application calls for a combination of techniques.

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Gaurav Verma, Rynaa Grover, Jiawei Zhou, Binny Mathew, Jordan Kraemer, Munmun Choudhury, Srijan Kumar

SELFOOD: Self-Supervised Out-Of-Distribution Detection via Learning to Rank (2023.findings-emnlp)

Challenge:	Using data from 420k Twitter posts, we characterize anti-Asian violence-provoking speech and collect a community-crowdsourced dataset to facilitate its large-scale detection.
Approach:	They develop a codebook to characterize anti-Asian violence-provoking speech and collect a community-crowdsourced dataset to facilitate its large-scale detection.
Outcome:	The proposed codebook analyzes 420k tweets over 3 years and compares classifiers with hateful speech classifier classifier to detect hateful content.

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Dheeraj Mekala, Adithya Samavedhi, Chengyu Dong, Jingbo Shang

Data Boost: Text Data Augmentation Through Reinforcement Learning Guided Conditional Generation (2020.emnlp-main)

Challenge:	Existing methods for supervised OOD detection require expensive manual annotation of in-distribution and OOD samples.
Approach:	They propose a self-supervised OOD detection method that requires only in-distribution samples as supervision.
Outcome:	Experiments with multiple classifiers on coarse- and fine-grained datasets show the proposed method performs well in both coarse-and fine-grid settings.

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Ruibo Liu, Guangxuan Xu, Chenyan Jia, Weicheng Ma, Lili Wang, Soroush Vosoughi

Annotating a Fable in Italian Sign Language (LIS) (2020.lrec-1)

Challenge:	Existing methods for data augmentation produce low readability or semantic consistency.
Approach:	They propose a framework which augments data through reinforcement learning guided conditional generation.
Outcome:	The proposed framework improves F1 performance on three different classification tasks by 8.7% on average when given only 10% of the whole data for training.

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Serena Trolvi, Rodolfo Delmonte

Challenge:	fables are short or medium-length stories with a moral and they generally have specific characteristics in SLs that are usually not to be found in spoken languages like Italian.
Approach:	They present work for automatic generation of a written text in Italian starting from glosses of fable in Italian Sign Language (LIS).
Outcome:	The proposed method was used to generate a written text in Italian starting from glosses of a fable in Italian Sign Language (LIS).

Robust Text Classification: Analyzing Prototype-Based Networks (2024.findings-emnlp)

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Zhivar Sourati, Darshan Deshpande, Filip Ilievski, Kiril Gashteovski, Sascha Saralajew

RedDust: a Large Reusable Dataset of Reddit User Traits (2020.lrec-1)

Challenge:	Language models exhibit a drop in performance on noisy data, which can cause classifiers to incorrectly change their predictions.
Approach:	They propose to use Prototype-Based Networks to classify examples based on their similarity to prototypical examples of a class (prototypes) they show that PBNs offer more robustness under both targeted and static adversarial attacks.
Outcome:	The proposed model is robust to noise and targets both targeted and static attacks.

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Anna Tigunova, Paramita Mirza, Andrew Yates, Gerhard Weikum

A Corpus of Turkish Offensive Language on Social Media (2020.lrec-1)

Challenge:	Social media is a rich source of assertions about personal traits, but identifying personal traits from implicit assertions is difficult because of the users’ highly varied vocabulary and expressions.
Approach:	They propose to build a large-scale annotated resource for user profiling for over 300k Reddit users across five attributes: profession, hobby, family status, age, and gender.
Outcome:	The proposed resource is the first annotated language resource about Reddit users at large scale.

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Çağrı Çöltekin

A Multi-Platform Arabic News Comment Dataset for Offensive Language Detection (2020.lrec-1)

Challenge:	Identifying abusive, offensive, aggressive or in general inappropriate language has recently attracted interest of researchers from academic as well as commercial institutions.
Approach:	They propose to classify Turkish offensive language corpus using state-of-the-art annotation methods . they find 19 % of tweets contain some type of offensive language .
Outcome:	The proposed corpus of Turkish offensive language is the first of its kind in the world . the results show that 19 % of the tweets contain some type of offensive language .

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Shammur Absar Chowdhury, Hamdy Mubarak, Ahmed Abdelali, Soon-gyo Jung, Bernard J. Jansen, Joni Salminen

Deep One-Class Hate Speech Detection Model (2022.lrec-1)

Challenge:	Social media platforms allow users to engage in conversation with limited accountability, causing hate crimes and mental harm to targeted individuals.
Approach:	They propose to make public a new dialectal Arabic news comment dataset . they analyze distinctive lexical content along with the use of emojis in offensive comments .
Outcome:	The proposed dataset analyzes offensive language and distinctive lexical content along with the use of emojis on Twitter, Facebook, and YouTube.

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Saugata Bose, Dr. Guoxin Su

PIEClass: Weakly-Supervised Text Classification with Prompting and Noise-Robust Iterative Ensemble Training (2023.emnlp-main)

Challenge:	Existing approaches to hate speech detection neglect distinct attributes of hate speeches from other sentimental types such as “aggressive” and “racist”.
Approach:	They propose a one-class model where the detection classifier is trained with hate-class samples only.
Outcome:	The proposed model outperforms existing models with four benchmark datasets and shows that it performs better than existing models.

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Yunyi Zhang, Minhao Jiang, Yu Meng, Yu Zhang, Jiawei Han

Don’t Retrain, Just Rewrite: Countering Adversarial Perturbations by Rewriting Text (2023.acl-long)

Challenge:	Existing methods for text classification use label names of target classes as the only supervision.
Approach:	They propose a method that uses keyword-based keyword matching to generate pseudo labels . they propose 'pieclass' module that iteratively trains classifiers and updates pseudo labels.
Outcome:	The proposed method achieves better performance than existing strong baselines on seven benchmark datasets and similar performance to fully-supervised classifiers on sentiment classification tasks.

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Ashim Gupta, Carter Blum, Temma Choji, Yingjie Fei, Shalin Shah, Alakananda Vempala, Vivek Srikumar

Multilingual Coarse Political Stance Classification of Media. The Editorial Line of a ChatGPT and Bard Newspaper (2023.findings-emnlp)

Challenge:	ATINTER model can be used to rewrite adversarial inputs to make them non-adversarial . if undefended, model should maintain good task performance and effectively mitigate adversarials .
Approach:	They propose a model that intercepts adversarial inputs and learns to rewrite them . they show that it provides better adversarial robustness than existing defense approaches .
Outcome:	The proposed model improves adversarial robustness without compromising task accuracy on a sentiment classification dataset.

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Cristina España-Bonet

CritiQ: Mining Data Quality Criteria from Human Preferences (2025.acl-long)

Challenge:	Several platforms currently rate news outlets according to their political bias, but they are not ready for use in the near future.
Approach:	They use ratings of authentic news outlets to create a multilingual corpus of news with coarse stance annotations along with automatically extracted topic annotations.
Outcome:	The proposed model can identify the editorial line of most unseen newspapers in English, German, Spanish and Catalan.

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Honglin Guo, Kai Lv, Qipeng Guo, Tianyi Liang, Zhiheng Xi, Demin Song, Qiuyinzhe Zhang, Yu Sun, Kai Chen, Xipeng Qiu, Tao Gui

A new European Portuguese corpus for the study of Psychosis through speech analysis (2022.lrec-1)

Challenge:	Existing methods to train language models rely on manual design, perplexity, or careful prompt engineering.
Approach:	They propose a method that automatically mines criteria from human preferences for data quality with only 30 human-annotated pairs and performs efficient data selection.
Outcome:	The proposed method improves on human-annotated test sets and shows high accuracy on code, math, and logic domains.

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Maria Forjó, Daniel Neto, Alberto Abad, HSofia Pinto, Joaquim Gago

Agreeing to Disagree: Annotating Offensive Language Datasets with Annotators’ Disagreement (2021.emnlp-main)

Challenge:	Psychosis is a clinical syndrome characterized by symptoms such as hallucinations, delusions, thought disorders and disorganized speech.
Approach:	They describe the creation of the first European Portuguese corpus for the identification of the presence of speech characteristics of psychosis.
Outcome:	The results show that spontaneous speech presents more identifiable characteristics than read speech to differentiate healthy and patients diagnosed with psychosis.

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Elisa Leonardelli, Stefano Menini, Alessio Palmero Aprosio, Marco Guerini, Sara Tonelli

A Closer Look at Multidimensional Online Political Incivility (2024.emnlp-main)

Challenge:	supervised learning is a key component of offensive language detection, but there is little attention given to the quality of annotated data.
Approach:	They propose to examine the level of agreement among annotators while selecting data to create offensive language datasets, a task involving a high level of subjectivity.
Outcome:	The proposed datasets show that annotators' agreement has a strong effect on classifiers performance and robustness.

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Sagi Pendzel, Nir Lotan, Alon Zoizner, Einat Minkov

Linguistic Theories

Challenge:	80% of the uncivil tweets are authored by 20% of the users, where users who are politically engaged are more inclined to use uncival language.
Approach:	They analysed 13K political tweets in the U.S. using crowd sourcing and classified them by their respective categories.
Outcome:	The proposed method enables us to characterise the distribution of incivility across users and geopolitical regions.

Finding the SWEET Spot: Analysis and Improvement of Adaptive Inference in Low Resource Settings (2023.acl-long)

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Daniel Rotem, Michael Hassid, Jonathan Mamou, Roy Schwartz

GitHub Typo Corpus: A Large-Scale Multilingual Dataset of Misspellings and Grammatical Errors (2020.lrec-1)

Challenge:	Pre-trained Transformer-based language models such as BERT, DeBERTa, and GPT3 have become the go-to tool in NLP.
Approach:	They propose an Early-Exit fine-tuning method that assigns each classifier its own set of unique model weights, not updated by other classifiers.
Outcome:	The proposed method outperforms Early-Exit and Multi-Model at fast speeds while maintaining comparable scores to Early- Exit at slow speeds.

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Masato Hagiwara, Masato Mita

Softmax Tree: An Accurate, Fast Classifier When the Number of Classes Is Large (2021.emnlp-main)

Challenge:	Lack of large-scale datasets has been a major hindrance to the development of NLP tasks such as spelling correction and grammatical error correction.
Approach:	They propose to make GitHub Typo Corpus a multilingual dataset of misspellings and grammatical errors available for use in NLP.
Outcome:	The proposed dataset contains more than 350k edits and 65M characters in more than 15 languages.

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Arman Zharmagambetov, Magzhan Gabidolla, Miguel A. Carreira-Perpinan

Re-examining Sexism and Misogyny Classification with Annotator Attitudes (2024.findings-emnlp)

Challenge:	Classification problems with thousands or more classes occur in NLP, for example language models or document classification.
Approach:	a new algorithm uses a binary tree with sparse hyperplanes and small softmax classifiers at the leaves to predict the top class.
Outcome:	The proposed model is faster at inference because the input follows a single path to a leaf and the softmax classifier operates on a small subset of the classes.

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Aiqi Jiang, Nikolas Vitsakis, Tanvi Dinkar, Gavin Abercrombie, Ioannis Konstas

BYOC: Personalized Few-Shot Classification with Co-Authored Class Descriptions (2023.findings-emnlp)

Challenge:	Existing datasets for content moderation fail to capture plurality of possible annotator perspectives or ensure representation of affected groups.
Approach:	They examine the relationship between annotator identities and attitudes and the responses they give to two GBV labelling tasks.
Outcome:	The results show that higher Right Wing Authoritarianism scores are associated with a higher propensity to label text as sexist . higher scores are also associated with negative attitudes towards sexism and neosexist attitudes .

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Arth Bohra, Govert Verkes, Artem Harutyunyan, Pascal Weinberger, Giovanni Campagna

MAGIC: Multi-Argument Generation with Self-Refinement for Domain Generalization in Automatic Fact-Checking (2024.lrec-main)

Challenge:	Existing approaches to text classification require large annotated corpora to train or long context to fit many examples.
Approach:	They propose a method to few-shot text classification using an LLM.
Outcome:	The proposed approach yields high accuracy classifiers within 79% of the performance of models trained with larger datasets while using only 1% of their training sets.

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Wei-Yu Kao, An-Zi Yen

Euphemistic Abuse – A New Dataset and Classification Experiments for Implicitly Abusive Language (2023.emnlp-main)

Challenge:	Existing methods for fact-checking are limited in retrieving evidence from documents . retrieved evidence derived from different sources strains generalization capabilities of classifiers .
Approach:	They propose a framework for cross-domain fact-checking using multi-argument generation . they propose to reconstruct concise evidence from large amounts of evidence retrieved from different sources .
Outcome:	The proposed framework is effective in identifying the veracity of out-of-domain claims . it can be used to extract evidence from documents and verify claims across domains .

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Michael Wiegand, Jana Kampfmeier, Elisabeth Eder, Josef Ruppenhofer

Unmasking Fake Careers: Detecting Machine-Generated Career Trajectories via Multi-layer Heterogeneous Graphs (2025.emnlp-main)

Challenge:	Currently, only explicit abuse can be reliably detected due to the increasing amount of abusive language on the Web.
Approach:	They propose a crowdsourced dataset that can detect euphemistic abuse by paraphrasing simple explicit utterances.
Outcome:	The proposed classifier augments training data with automatically-generated GPT-3 completions.

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Michiharu Yamashita, Thanh Tran, Delvin Ce Zhang, Dongwon Lee

Comparing a BERT Classifier and a GPT classifier for Detecting Connective Language Across Multiple Social Media (2024.emnlp-main)

Challenge:	Large Language Models (LLMs) generate convincing career trajectories in fake resumes . a novel heterogeneous, hierarchical multi-layer graph framework is proposed to model career entities and their relations in a unified global graph built from genuine resumes.
Approach:	They propose a novel heterogeneous, hierarchical multi-layer graph framework that models career entities and their relations in a unified global graph built from genuine resumes.
Outcome:	The proposed framework outperforms state-of-the-art models by 5.8-85.0% relative to baselines.

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Josephine Lukito, Bin Chen, Gina Masullo, Natalie Stroud

DISCERN: Decoding Systematic Errors in Natural Language for Text Classifiers (2024.emnlp-main)

Challenge:	Using social media, researchers have built a variety of text classifiers to understand short-form text . however, there is little discussion regarding what desired language on social media would look like .
Approach:	They propose an approach for detecting connective language from social media discussions using BERT and GPT-3.5 turbo.
Outcome:	The proposed classifier outperforms the existing classifiers in detecting connective language from social media discussions.

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Rakesh R Menon, Shashank Srivastava

Explainability & Interpretability in NLP

Challenge:	Recent work proposes automatic methods for identifying and explaining systematic biases using keywords.
Approach:	They propose automatic methods for identifying and explaining systematic biases using keywords.
Outcome:	The proposed framework improves classifiers by augmenting training sets with synthetically generated instances or annotated examples via active learning.

A Classifier of Word-Level Variants in Witnesses of Biblical Hebrew Manuscripts (2025.findings-acl)

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Iglika Nikolova-Stoupak, Maxime Amblard, Sophie Robert-Hayek, Frédérique Rey

FanChuan: A Multilingual and Graph-Structured Benchmark For Parody Detection and Analysis (2025.findings-acl)

Challenge:	a strong classifier (F1 value of 0.80) is trained to predict the category of difference between word pairs as present in collated (aligned) pairs of witnesses.
Approach:	The project is based on the relationship between available witnesses of biblical Hebrew and a strong classifier (F1 value of 0.80) is trained to predict the category of difference between word pairs as present in collated pairs of witnesses.
Outcome:	The proposed model is non-neural and uses part-of-speech tags, hand-crafted rules per category and synthetically derived data.

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Yilun Zheng, Sha Li, Fangkun Wu, Yang Ziyi, Lin Hongchao, Zhichao Hu, Cai Xinjun, Ziming Wang, Jinxuan Chen, Sitao Luan, Jiahao Xu, Lihui Chen

Reference-guided Style-Consistent Content Transfer (2024.lrec-main)

Challenge:	Parody is an emerging phenomenon on social media, where individuals imitate a role or position opposite to their own . limited available data and deficient diversity in current datasets hinder study of parody .
Approach:	They build a dataset of parody users and annotated comments from both English and Chinese corpora to test parody detection and comment sentiment analysis.
Outcome:	The proposed datasets provide richer contextual information, which is lacking in existing datasets.

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Wei-Fan Chen, Milad Alshomary, Maja Stahl, Khalid Al Khatib, Benno Stein, Henning Wachsmuth

On the Fragility of Active Learners for Text Classification (2024.emnlp-main)

Challenge:	Text style transfer involves changing the style of a text while preserving its original style.
Approach:	They propose a task of style-consistent content transfer which involves modifying a text’s content based on a provided reference statement while preserving its original style.
Outcome:	The proposed approach meets three important conditions: reference faithfulness, style adherence, and coherence.

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Abhishek Ghose, Emma Nguyen

HomoGraphAdapter: A Homogeneous Graph Neural Network as an Effective Adapter for Vision-Language Models (2025.findings-emnlp)

Challenge:	Active learning (AL) techniques optimally utilize a labeling budget by iteratively selecting instances that are most valuable for learning.
Approach:	They propose to use active learning techniques to iteratively select instances that are most valuable for learning.
Outcome:	The proposed framework is used to benchmark active learning techniques for text classification using pre-trained representations.

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Chuan He, Zhuozhao Li, Song Guo, Xiaocheng Lu, Jinxiang Lai

SM-FEEL-BG - the First Bulgarian Datasets and Classifiers for Detecting Feelings, Emotions, and Sentiments of Bulgarian Social Media Text (2024.lrec-main)

Challenge:	Existing adaptation methods overlook structural knowledge between text and image modalities or create overly complex graphs containing redundant information for alignment.
Approach:	They propose a method to adapt visual models to downstream tasks using text and image modalities.
Outcome:	The proposed method improves classification accuracy by 1.51% for 1-shot and 0.74% for 16-shot on 11 datasets.

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Irina Temnikova, Iva Marinova, Silvia Gargova, Ruslana Margova, Alexander Komarov, Tsvetelina Stefanova, Veneta Kireva, Dimana Vyatrova, Nevena Grigorova, Yordan Mandevski, Stefan Minkov

SPACE-IDEAS: A Dataset for Salient Information Detection in Space Innovation (2024.lrec-main)

Challenge:	SM-FEEL-BG is the first Bulgarian-language package for emotion detection and sentiment analysis.
Approach:	They introduce SM-FEEL-BG, a Bulgarian-language package that contains 6 datasets with Social Media (SM) texts with emotion, feeling, and sentiment labels and 4 classifiers trained on them.
Outcome:	The proposed package is the first to be released in Bulgarian and is available for free.

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Andres Garcia-Silva, Cristian Berrio, Jose Manuel Gomez-Perez

Chinese Inertial GAN for Handwriting Signal Generation and Recognition (2025.acl-long)

Challenge:	Detecting salient parts in text is widely used to mitigate information overload.
Approach:	They propose a dataset for salient information detection from space innovation that is manually annotated using a large generative language model.
Outcome:	The proposed dataset can be leveraged using multitask learning to train better classifiers.

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Yifeng Wang, Yi Zhao

Task-Oriented Paraphrase Analytics (2024.lrec-main)

Challenge:	Inertial sensors can measure the acceleration and angular velocity of moving objects and are widely used in electronic devices such as smartphones, smartwatches, and fitness bands.
Approach:	They propose to use Chinese glyph encoding, forced optimal transport, and semantic relevance alignment to acquire unlimited training samples for Chinese inertial writing recognition.
Outcome:	The proposed system improves the performance of six widely used classifiers from 6.7% to 98.4%.

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Marcel Gohsen, Matthias Hagen, Martin Potthast, Benno Stein

Text Generation

Challenge:	Existing studies on paraphrasing have applied different criteria to the task . authors have previously unmasked related tasks as paraphrases .
Approach:	They propose a taxonomy to organize 25 identified paraphrasing tasks . authors propose to use classifiers to identify tasks that a given paraphrased instance fits .
Outcome:	The proposed taxonomy identifies 25 paraphrasing tasks that fit the proposed task.

Text Filtering Classifiers for Medium-Resource Languages (2024.lrec-main)

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Jón Daðason, Hrafn Loftsson

Class Distillation with Mahalanobis Contrast: An Efficient Training Paradigm for Pragmatic Language Understanding Tasks (2025.acl-long)

Challenge:	linguistic and NLP researchers use web-crawled corpora to filter low-quality texts . early Transformer-based language models were typically pre-trained on curated corporum .
Approach:	They compare the effectiveness of various text filtering classifiers on Icelandic, Estonian and Basque texts . they use a perplexity-based classifier and a self-supervised classifier trained on TQ-IS to discern between documents from curated and web-crawled corpora.
Outcome:	The proposed classifiers achieve F1 scores of 94.48%, 99.01% and 93.40% on the Icelandic, Estonian and Basque datasets.

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Chenlu Wang, Weimin Lyu, Ritwik Banerjee

Dialectical Structured Reasoning for Explainable Multimodal Fake News Detection (2026.findings-acl)

Challenge:	Existing classifiers for detecting deviant language often come with significant computational cost and high data demands.
Approach:	They propose a class-disstillation paradigm that targets the core challenge: distilling a small, well-defined target class from a heterogeneous background.
Outcome:	The proposed training paradigm outperforms baselines and large language models on three benchmarks.

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Ruichao Yang, Yufan Bian, Wei Gao, Bo-Wen Zhang, Jing Ma, Hongzhan Lin, Ziyang Luo, Xiaobin Zhu, Xu-Cheng Yin

No One Fits All: From Fixed Prompting to Learned Routing in Multilingual LLMs (2026.findings-acl)

Challenge:	Existing fake news detection models are opaque and lack deductive transparency . a framework for dialectical structured reasoning is proposed to address this limitation .
Approach:	They propose a framework that model fake news detection as an explicit dialectical process over multimodal social context.
Outcome:	The proposed framework achieves state-of-the-art while producing transparent explanations that mirror human reasoning process.

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Wei-Chi Wu, Sheng-Lun Wei, Hen-Hsen Huang, Hsin-Hsi Chen

Empathy Applicability Modeling for General Health Queries (2026.findings-acl)

Challenge:	Existing studies show that translation-based prompting is not universally optimal for multilingual LLMs.
Approach:	They evaluate translation-based prompting across ten languages and four benchmarks . they propose a lightweight classifier that predicts whether native or translation- based prompts are optimal .
Outcome:	The proposed classifiers achieve statistically significant improvements over fixed prompting strategies across ten languages and four benchmarks.

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Shan Randhawa, Agha Ali Raza, Kentaro Toyama, Julie Hui, Mustafa Naseem