CLASSIFICATION ACCURACY

IDP Accelerator: Agentic Document Intelligence from Extraction to Compliance Validation (2026.acl-demo)

Challenge:	Evaluating translation models is a trade-off between effort and detail.
Approach:	They propose to use a neural text classifier to automatically expose systematic differences between human and machine translations to human experts.
Outcome:	The proposed method exposes systematic differences between human and machine translations to human experts.

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Md Mofijul Islam, Md Sirajus Salekin, Joe King, Priyashree Roy, Vamsi Thilak Gudi, Spencer Romo, Akhil Nooney, Bob Strahan, Boyi Xie, Diego A. Socolinsky

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

Challenge:	Large Language Models (LLMs) are inadequate for extracting structured insights from unstructured documents.
Approach:	They propose a framework enabling agentic AI for end-to-end document intelligence with four key components: DocSplit, configurable Extraction Module, and Rule Validation Module.
Outcome:	The proposed framework achieves 98% classification accuracy, 80% reduced processing latency, and 77% lower operational costs over legacy baselines.

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Christopher Schröder, Lydia Müller, Andreas Niekler, Martin Potthast

Long-Distance Dependencies Don’t Have to Be Long: Simplifying through Provably (Approximately) Optimal Permutations (P19-2)

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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Rishi Bommasani

Detecting Response Generation Not Requiring Factual Judgment (2024.naacl-srw)

Challenge:	Neural models at the sentence level often need to model the interaction between words . however, there is no guarantee that the standard ordering of words is computationally efficient or optimal .
Approach:	They propose to use a dependency parse as a proxy for inter-word dependencies in a sentence to simplify the sentence with combinatorial objectives imposed on the sentence-parse pair.
Outcome:	The proposed model improves classification accuracy and reduces classification error by 2.0% over the previous state of the art.

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Ryohei Kamei, Daiki Shiono, Reina Akama, Jun Suzuki

Dialogue Systems & Conversational Agents

Challenge:	Large language models (LLMs) have undergone considerable development and can solve various natural language processing tasks.
Approach:	They aimed to achieve both attractiveness and factuality in a dialogue response by crowdsourcing a dataset and performing classification tasks on several models.
Outcome:	The proposed model with the highest classification accuracy could yield about 88% accurate classification results.

User Interest Modelling in Argumentative Dialogue Systems (2022.lrec-1)

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Annalena Aicher, Nadine Gerstenlauer, Wolfgang Minker, Stefan Ultes

Dialogue Systems & Conversational Agents

Challenge:	Existing studies on user interest in dialogue systems depend on explicit user feedback.
Approach:	They propose a model to implicitly estimate user interest during argumentative dialogues based on semantically clustered data.
Outcome:	The proposed model achieves a classification accuracy of 74.9% and tested with different Artificial Neural Networks (ANN) which new argument would fit the user interest best.

Can Large Language Models Serve as Effective Classifiers for Hierarchical Multi-Label Classification of Scientific Documents at Industrial Scale? (2025.coling-industry)

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Seyed Amin Tabatabaei, Sarah Fancher, Michael Parsons, Arian Askari

Learning Domain Terms - Empirical Methods to Enhance Enterprise Text Analytics Performance (2020.coling-industry)

Challenge:	Large Language Models (LLMs) have demonstrated great potential in complex tasks such as multi-label classification, but the vast number of labels can exceed LLMs’ input limits.
Approach:	They propose a method that integrates large language models with dense retrieval techniques to overcome these challenges.
Outcome:	The proposed methods avoid frequent retraining by leveraging zero-shot and few-shot learning for real-time label assignment.

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Gargi Roy, Lipika Dey, Mohammad Shakir, Tirthankar Dasgupta

Model-agnostic Methods for Text Classification with Inherent Noise (2020.coling-industry)

Challenge:	Standard text analytics algorithms are degraded on consumer generated data, a study finds . enterprise data contains domain terms that are not available in general purpose thesaurus .
Approach:	They propose unsupervised methods for cleaning noisy repositories after domain terms are segregated from true noise terms.
Outcome:	The proposed method improves classification accuracy after noise correction . it is based on a systematic analysis of enterprise data .

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Kshitij Tayal, Rahul Ghosh, Vipin Kumar

Multitask Semi-Supervised Learning for Class-Imbalanced Discourse Classification (2021.emnlp-main)

Challenge:	Text classification is a fundamental problem in natural language processing, but its performance relies on high-quality annotations.
Approach:	They propose to use model-agnostic methods to handle inherent noise in large scale text classification that can be easily incorporated into existing machine learning workflows with minimal interruption.
Outcome:	The proposed method outperforms baselines by up to 10% in classification accuracy while requiring no network modifications.

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Alexander Spangher, Jonathan May, Sz-Rung Shiang, Lingjia Deng

A Simple yet Efficient Prompt Compression Method for Text Classification Data Annotation Using LLM (2025.coling-industry)

Challenge:	Discourse learning is a complex task, and schemas evolve across annotation efforts preventing compilation of smaller datasets into larger ones.
Approach:	They propose a multitask learning approach that can combine discourse datasets from similar and diverse domains to improve discourse classification.
Outcome:	The proposed approach improves on the NewsDiscourse dataset by 4.9% over current state-of-the-art benchmarks on one of the largest discourse datasets.

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Yiran Xie, Debin Xiao, Ping Wang, Shuming Liu

A Multi-Dimensional, Cross-Domain and Hierarchy-Aware Neural Architecture for ISO-Standard Dialogue Act Tagging (2022.coling-1)

Challenge:	Existing methods to improve the accuracy of large language models (LLMs) are often impractical due to high costs and time consumption.
Approach:	They propose a method that uses keyword extraction to reduce prompt tokens in text annotation tasks.
Outcome:	The proposed method reduces prompt tokens while maintaining high accuracy.

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Stefano Mezza, Wayne Wobcke, Alan Blair

Dialogue Systems & Conversational Agents

Challenge:	Dialogue Act tagging with ISO 24617-2 standard is a difficult task that requires multiple labels covering semantic, syntactic and pragmatic aspects of dialogue.
Approach:	They propose a neural architecture to increase Dialogue Act tagging accuracy by using low-frequency fine-grained tags.
Outcome:	The proposed model achieves state-of-the-art tagging results on DialogBank data set . it uses syntactic information in the form of Part-Of-Speech and dependency tags .

A Knowledge-Augmented Neural Network Model for Implicit Discourse Relation Classification (C18-1)

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Yudai Kishimoto, Yugo Murawaki, Sadao Kurohashi

Exploring the Role of BERT Token Representations to Explain Sentence Probing Results (2021.emnlp-main)

Challenge:	Existing studies on implicit discourse relation classification have shown success using feedforward networks and convolutional neural networks.
Approach:	They propose to augment input text with external knowledge and context and adopt a neural network model that can effectively handle the augmented text.
Outcome:	The proposed model outperforms existing models on implicit discourse relation classification.

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Hosein Mohebbi, Ali Modarressi, Mohammad Taher Pilehvar

LegalSeg: Unlocking the Structure of Indian Legal Judgments Through Rhetorical Role Classification (2025.findings-naacl)

Challenge:	Recent studies have focused on enhancing existing models with the primary objective of improving downstream performance on various NLP tasks.
Approach:	They propose to use BERT to encode meaningful knowledge in token representations to explain probing results.
Outcome:	The proposed model can detect syntactic and semantic abnormalities and distinguish between grammatical number and tense subspaces.

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Shubham Kumar Nigam, Tanmay Dubey, Govind Sharma, Noel Shallum, Kripabandhu Ghosh, Arnab Bhattacharya

Does Topic Sentiment Cause Perceived Ideology? Comparing Human and LLM Annotations in Political News Articles (2026.acl-srw)

Challenge:	a lack of large-scale annotated datasets hinders effective training of ML models . despite advances in semantic segmentation, challenges persist in distinguishing between closely related roles .
Approach:	They propose a large annotated dataset for semantic segmentation of legal documents . they use a rhetorical role classification model to compare performance against other models .
Outcome:	The largest annotated dataset for this task outperforms models relying on sentence-level features.

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Upasana Chatterjee

Conflict and Overlap Classification in Construction Standards Using a Large Language Model (2025.naacl-industry)

Challenge:	Existing validation studies assess output-level agreement; none test causal structure of LLM decisions mirrors that of human decisions.
Approach:	They compare topic sentiment and ideology labels using human annotators . they say this is evidence of shortcut learning by fine-tuning on ideology-labeled data .
Outcome:	The proposed model internalises a spurious sentiment–ideology coupling not operative in human judgment for this task.

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Seong-Jin Park, Youn-Gyu Jin, Hyun-Young Moon, Choi Bong-Hyuck, Lee Seung Hwan, Ohjoon Kwon, Kang-Min Kim

Linguistically Motivated Features for Classifying Shorter Text into Fiction and Non-Fiction Genre (2022.coling-1)

Challenge:	Current manual approaches to analyzing overlapping or conflicting content are time-consuming, costly, and error-prone.
Approach:	They propose a large language model that uses a construction domain-adapted large language for the semantic comparison of sentences in construction standards.
Outcome:	The proposed framework achieves 97.9% accuracy and 0.907 macro F1-score in classifying sentences from Korean construction standards as overlapping, conflicting, or neutral.

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Arman Kazmi, Sidharth Ranjan, Arpit Sharma, Rajakrishnan Rajkumar

Exploiting contextual information to improve stance detection in informal political discourse with LLMs (2025.acl-srw)

Challenge:	linguistically motivated features are used to classify paragraph-level text into fiction and non-fiction genres.
Approach:	They deploy linguistically motivated features to classify paragraph-level text into fiction and non-fiction genres using a logistic regression model.
Outcome:	The proposed model gives 15.56% accuracy jump over baseline model . the proposed model also transfers over to another dataset, Baby BNC corpus .

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Arman Engin Sucu, Yixiang Zhou, Mario A. Nascimento, Tony Mullen

Sentiment Analysis

Challenge:	Political stance detection is an increasingly relevant part of analyzing the flow of ideas in online environments where discourse is informal and implicitly expressed.
Approach:	They evaluate large language models for political stance detection in informal online discourse by analyzing user profiles derived from historical posts.
Outcome:	The proposed model improves accuracy by up to 74% on a political forum dataset.

Labeled Anchors and a Scalable, Transparent, and Interactive Classifier (D18-1)

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Jeffrey Lund, Stephen Cowley, Wilson Fearn, Emily Hales, Kevin Seppi

Enhancing Fine-Grained Image Classifications via Cascaded Vision Language Models (2024.findings-emnlp)

Challenge:	Labeled Anchors is an interactive and supervised topic model based on the anchor words algorithm .
Approach:	They propose an interactive supervised topic model based on the anchor words algorithm . they propose a classifier which requires no training beyond topic inference .
Outcome:	The proposed model is human-interpretable and fast, and can be interactive.

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Canshi Wei

Generating Natural Language Adversarial Examples through Probability Weighted Word Saliency (P19-1)

Challenge:	Fine-grained image classification is a challenge for vision-language models (VLMs) such as CLIP, which struggle to distinguish between semantically similar classes due to insufficient supervision for fine-grain tasks.
Approach:	They propose a framework that harnesses the complementary strengths of both CLIP-like and LVLMs to tackle these challenges.
Outcome:	The proposed framework outperforms existing models on multiple fine-grained datasets, particularly the Stanford Cars dataset.

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Shuhuai Ren, Yihe Deng, Kun He, Wanxiang Che

Boosting Text Augmentation via Hybrid Instance Filtering Framework (2023.findings-acl)

Challenge:	Existing approaches to attack text classification are limited due to the complexity of the problem.
Approach:	They propose a greedy algorithm to generate adversarial examples that maintain lexical correctness, grammatical correctiness and semantic similarity.
Outcome:	The proposed algorithm maintains lexical correctness, grammatical correctity and semantic similarity well and is hard for humans to perceive.

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Heng Yang, Ke Li

Handling Ambiguity in Emotion: From Out-of-Domain Detection to Distribution Estimation (2024.acl-long)

Challenge:	Existing text augmentation methods generate instances with shifted feature spaces, which leads to a drop in performance on large datasets.
Approach:	They propose a hybrid instance-filtering framework that generates instances with shifted feature spaces, which leads to a drop in performance on augmented data.
Outcome:	The proposed framework outperforms state-of-the-art methods on three classification tasks and nine public datasets.

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Wen Wu, Bo Li, Chao Zhang, Chung-Cheng Chiu, Qiujia Li, Junwen Bai, Tara Sainath, Phil Woodland

Machine Translation for Machines: the Sentiment Classification Use Case (D19-1)

Challenge:	Experimental results show that incorporating utterances without majority-agreed labels into an additional class reduces the classification performance of the other emotion classes.
Approach:	They propose to combine utterances without majority-agreed labels into an additional class . they propose to quantify uncertainty in emotion classification using evidential deep learning .
Outcome:	The proposed method retains classification accuracy while effectively detects ambiguous emotion expressions.

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Amirhossein Tebbifakhr, Luisa Bentivogli, Matteo Negri, Marco Turchi

Syntactical Analysis of the Weaknesses of Sentiment Analyzers (D18-1)

Challenge:	Traditionally, machine translation (MT) pursues a "human-oriented" objective: generating fluent output for a downstream task.
Approach:	They propose a neural machine translation approach that uses weak feedback to generate translations that are best suited for a downstream task.
Outcome:	The proposed approach outperforms general-purpose models and reinforcement learning methods on German and Italian tweets.

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Rohil Verma, Samuel Kim, David Walter

Sentiment Analysis

Challenge:	polarity items are lexical items that can only appear in specific licensing contexts.
Approach:	They analyze sentiment analyzers for negative polarity items and a set of 150 test sentences . they describe a syntactic phenomenon that an ideal sentiment analyzeur must understand .
Outcome:	The proposed method focuses on two sentential structures: downward entailment and non-monotone quantifiers.

When in Doubt: Improving Classification Performance with Alternating Normalization (2021.findings-emnlp)

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Menglin Jia, Austin Reiter, Ser-Nam Lim, Yoav Artzi, Claire Cardie

Identifying Self-Disclosures of Use, Misuse and Addiction in Community-based Social Media Posts (2024.findings-naacl)

Challenge:	a classifier that uses a nonparametric post-processing step for classification suffers when given examples that are close to its decision boundary.
Approach:	They propose a nonparametric post-processing step that re-adjusts predicted class probability distributions using high-confidence validation examples.
Outcome:	The proposed method improves classifier accuracy on difficult examples.

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Chenghao Yang, Tuhin Chakrabarty, Karli Hochstatter, Melissa Slavin, Nabila El-Bassel, Smaranda Muresan

Using Deep Neural Networks with Intra- and Inter-Sentence Context to Classify Suicidal Behaviour (2020.lrec-1)

Challenge:	Experimental results show that identifying the phases of opioid use disorder is highly contextual and challenging.
Approach:	They analyze 2500 opioid-related posts from various subreddits labeled with six different phases of opioid use . they annotate span-level extractive explanations and critically evaluate state-of-the-art models in a supervised, few-shot, or zero-shot setting.
Outcome:	The proposed models improve classification accuracy and quality of the extracted explanations.

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Xingyi Song, Johnny Downs, Sumithra Velupillai, Rachel Holden, Maxim Kikoler, Kalina Bontcheva, Rina Dutta, Angus Roberts

In-Context Example Ordering Guided by Label Distributions (2024.findings-naacl)

Challenge:	Mental health problems are a major risk factor for suicide attempts.
Approach:	They propose to integrate information from sentences to left and right of the target sentence into the model to improve classification accuracy.
Outcome:	The proposed model was able to classify suicidal behaviour in autism spectrum disorder patient records significantly better than previous approaches.

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Zhichao Xu, Daniel Cohen, Bei Wang, Vivek Srikumar

Measuring the Impact of Readability Features in Fake News Detection (2020.lrec-1)

Challenge:	Prior work has shown that ICL is sensitive to different natural language instructions and different orderings of in-context examples.
Approach:	They propose two principles for in-context example ordering guided by model’s probability predictions.
Outcome:	The proposed model outperforms baseline models on 13 text classification datasets and nine autoregressive LLMs with 700M to 13B parameters.

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Roney Santos, Gabriela Pedro, Sidney Leal, Oto Vale, Thiago Pardo, Kalina Bontcheva, Carolina Scarton

Ethical NLP

Challenge:	Recent efforts to detect fake news use language-based approaches to detect news articles . authors show that readability features can improve classification accuracy .
Approach:	They propose to use readability features to detect fake news in the Brazilian Portuguese language . they show that such features can achieve up to 92% classification accuracy .
Outcome:	The proposed features achieve up to 92% accuracy and may improve previous classification results.

Investigating the Working of Text Classifiers (C18-1)

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Devendra Sachan, Manzil Zaheer, Ruslan Salakhutdinov

Efficient Strategies for Hierarchical Text Classification: External Knowledge and Auxiliary Tasks (2020.acl-main)

Challenge:	Text classification is one of the most widely studied tasks in natural language processing.
Approach:	They propose to use large multilayer neural network models to compose meaning of sentences . they propose to disincentivize focusing on key lexicons to improve classification accuracy .
Outcome:	The proposed models learn to compose the meaning of the sentences or focus on key lexicons for classifying the document.

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Kervy Rivas Rojas, Gina Bustamante, Arturo Oncevay, Marco Antonio Sobrevilla Cabezudo

Peer-Label Assisted Hierarchical Text Classification (2023.acl-long)

Challenge:	Hierarchical text classification is a complex task that requires extended training time and a large number of parameters.
Approach:	They propose a top-up-classification task using dictionaries and auxiliary task from external dictionary definitions.
Outcome:	The proposed method outperforms previous studies using a reduced number of parameters in two well-known English datasets.

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Junru Song, Feifei Wang, Yang Yang

MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages (2023.acl-long)

Challenge:	Existing approaches to hierarchical text classification focus on parent-child relationships . however, some texts with a category hierarchy also have latent relevancy among labels in the same level of the hierarchy.
Approach:	They propose a method to analyze latent relevancy of peer labels and a sample importance learning method to ameliorate the side effects.
Outcome:	The proposed method improves the latent relevancy of peer labels on standard datasets.

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Jack FitzGerald, Christopher Hench, Charith Peris, Scott Mackie, Kay Rottmann, Ana Sanchez, Aaron Nash, Liam Urbach, Vishesh Kakarala, Richa Singh, Swetha Ranganath, Laurie Crist, Misha Britan, Wouter Leeuwis, Gokhan Tur, Prem Natarajan

Multilinguality

Challenge:	We present the MASSIVE dataset–Multilingual Amazon Slu resource package (SLURP) for Slot-filling, Intent classification, and Virtual assistant evaluation.
Approach:	They present a 1M-example dataset of Amazon Slu utterances . they localize the dataset into 50 typologically diverse languages .
Outcome:	The proposed model includes exact match accuracy, intent classification accuracy, and slot-filling F1 score.

CLoSE: Contrastive Learning of Subframe Embeddings for Political Bias Classification of News Media (2022.coling-1)

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Michelle YoungJin Kim, Kristen Marie Johnson

Extracting and Analysing Metaphors in Migration Media Discourse: towards a Metaphor Annotation Scheme (2022.lrec-1)

Challenge:	Framing is a political strategy in which journalists and politicians emphasize certain aspects of an issue to influence and sway public opinion.
Approach:	They propose a BERT-based model which embeds indicators of frames from news articles in order to predict political bias.
Outcome:	The proposed model performs on subframes and political bias classification tasks and is able to detect political bias on both zero-shot and few-shot learning tasks.

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Ana Zwitter Vitez, Mojca Brglez, Marko Robnik Šikonja, Tadej Škvorc, Andreja Vezovnik, Senja Pollak

Challenge:	Using metaphors in media discourse is an increasingly researched topic . media are an important shaper of social reality and metaphors indicate how we think about issues through references to other things.
Approach:	They propose a neural transfer learning method for detecting metaphorical sentences in Slovene . scheme can be used for future metaphor annotations of other socially relevant topics .
Outcome:	The proposed method can be used for future metaphor annotations of other socially relevant topics.

vONTSS: vMF based semi-supervised neural topic modeling with optimal transport (2023.findings-acl)

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Weijie Xu, Xiaoyu Jiang, Srinivasan Sengamedu Hanumantha Rao, Francis Iannacci, Jinjin Zhao

Emotion Classification by Jointly Learning to Lexiconize and Classify (2020.coling-main)

Challenge:	Recent Neural Topic Models (NTMs) have limited applications in the real world due to the challenge of incorporating human knowledge.
Approach:	They propose a semi-supervised neural topic modeling method, vONTSS, which uses von Mises-Fisher variational autoencoders and optimal transport.
Outcome:	The proposed method outperforms existing semi-supervised topic modeling methods on multiple aspects.

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Deyu Zhou, Shuangzhi Wu, Qing Wang, Jun Xie, Zhaopeng Tu, Mu Li

Platt-Bin: Efficient Posterior Calibrated Training for NLP Classifiers (2022.findings-acl)

Challenge:	Existing approaches to identify emotions in short text are limited and lack coverage and inaccuracies when applied to informal short text.
Approach:	They propose a novel emotional network to jointly learn sentence emotions and construct emotion lexicons which are dynamically adapted to a given context.
Outcome:	The proposed model outperforms several approaches proposed in previous studies and achieves new state-of-the-art on the benchmark Twitter dataset.

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Rishabh Singh, Shirin Goshtasbpour

Towards Debiasing Translation Artifacts (2022.naacl-main)

Challenge:	Existing methods for posterior calibration return uncalibrated estimations of class posteriors, thus leading to poorer generalization.
Approach:	They propose an end-to-end trained calibrator that directly optimizes the objective while minimizing the difference between predicted and empirical posterior probabilities.
Outcome:	The proposed calibrator reduces calibration error and improves performance on benchmark NLP classification tasks.

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Koel Dutta Chowdhury, Rricha Jalota, Cristina España-Bonet, Josef Genabith

Multilinguality

Challenge:	Existing studies show translation artifacts in translations influence performance of cross-lingual tasks.
Approach:	They propose a method to reduce translation artifacts by extending an established bias-removal technique.
Outcome:	The proposed method reduces translationese at sentence and word level . it is the first study to debias translations on a natural language inference task .

FastClass: A Time-Efficient Approach to Weakly-Supervised Text Classification (2022.emnlp-main)

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Tingyu Xia, Yue Wang, Yuan Tian, Yi Chang

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy (2025.coling-main)

Challenge:	Recent research shows keyword-driven methods can achieve state-of-the-art performance on various tasks.
Approach:	They propose an efficient weakly-supervised text classification approach using unlabeled data . they use dense text representation to retrieve class-relevant documents from unlabed corpus .
Outcome:	The proposed weakly-supervised classification method outperforms keyword-driven models on a wide range of classification tasks.

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Min Zeng, Caiquan Liu, Shiqi Zhang, Li Xie, Chen Sang, Xiaoxin Chen

Improving End-to-End Speech Processing by Efficient Text Data Utilization with Latent Synthesis (2023.findings-emnlp)

Challenge:	Existing methods for text classification based on large language models are difficult to apply directly to solve.
Approach:	They propose a data quality enhancement method to improve LLMs' performance in classification tasks by using a greedy algorithm to select data and then performing fine-tuning.
Outcome:	The proposed method improves the performance of large language models in text classification tasks and significantly improves training efficiency, saving nearly half of the training time.

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Jianqiao Lu, Wenyong Huang, Nianzu Zheng, Xingshan Zeng, Yu Yeung, Xiao Chen

ToxiTrace: Gradient-Aligned Training for Explainable Chinese Toxicity Detection (2026.findings-acl)

Challenge:	Latent Synthesis is an efficient textual data utilization framework for end-to-end speech processing models . labeled speech data are scarcer and more expensive for collection compared to textual ones .
Approach:	They propose a textual data utilization framework for E2E speech processing models . they train a latent synthesizer to convert textual information into an intermediate latent representation .
Outcome:	The proposed framework improves on low-resource speech recognition and spoken language understanding tasks.

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Boyang Li, Hongzhe Shou, Yuanyuan Liang, JingBin Zhang, Fang Zhou

The Multilingual Amazon Reviews Corpus (2020.emnlp-main)

Challenge:	Existing toxic content detection methods focus on sentence-level classification but fail to provide readable and contiguous toxic evidence spans.
Approach:	They propose an explainability-oriented method for Chinese toxic content detection methods . they refine saliency cues into fine-grained toxic spans with lightweight LLM guidance .
Outcome:	The proposed method improves classification accuracy and toxic span extraction while preserving efficient encoder-based inference and producing more coherent explanations.

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Phillip Keung, Yichao Lu, György Szarvas, Noah A. Smith

Efficient Sentence Embedding using Discrete Cosine Transform (D19-1)

Challenge:	The corpus contains reviews in English, Japanese, German, French, Spanish, and Chinese, which were collected between 2015 and 2019 .
Approach:	They propose to use mean absolute error (MAE) instead of classification accuracy for this task since MAE accounts for ordinal nature of the ratings.
Outcome:	The proposed model uses mean absolute error (MAE) instead of classification accuracy since MAE accounts for ordinal nature of the ratings.

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Nada Almarwani, Hanan Aldarmaki, Mona Diab

Bridging Modalities: Enhancing Cross-Modality Hate Speech Detection with Few-Shot In-Context Learning (2024.emnlp-main)

Challenge:	Modern NLP systems rely on word embeddings as input units to encode statistical semantic and syntactic properties of words.
Approach:	They propose to use discrete cosine transform to compress word sequences in order-preserving manner.
Outcome:	The proposed model preserves syntactic information in semantic probing tasks . it is comparable to vector averaging but mediocre in performance.

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Ming Shan Hee, Aditi Kumaresan, Roy Lee

Leveraging Task Transferability to Meta-learning for Clinical Section Classification with Limited Data (2022.acl-long)

Challenge:	Recent research has developed models targeting specific modalities but lacks transferability between formats.
Approach:	They conduct extensive experiments using few-shot in-context learning with large language models to explore the transferability of hate speech detection between modalities.
Outcome:	The proposed model outperforms vision-language demonstrations in few-shot learning settings.

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Zhuohao Chen, Jangwon Kim, Ram Bhakta, Mustafa Sir

An Analysis of Dataset Overlap on Winograd-Style Tasks (2020.coling-main)

Challenge:	Existing text classification systems require thousands of in-domain text data to achieve high performance.
Approach:	They propose an algorithm to improve task transferability of meta-learning-based text classification by normalizing negative conditional entropy from source task data to boost cross-domain meta- learning accuracy.
Outcome:	The proposed method improves section classification accuracy significantly compared to meta-learning algorithms.

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Ali Emami, Kaheer Suleman, Adam Trischler, Jackie Chi Kit Cheung

Class Name Guided Out-of-Scope Intent Classification (2024.findings-emnlp)

Challenge:	a large number of test instances overlap considerably with pretraining corpora, a study finds . for a number of years, models struggled to exceed chance-level performance .
Approach:	They analyze the effects of varying degrees of overlaps that occur between pretraining corpora and test instances in WSC-style tasks.
Outcome:	The WSC-Web dataset is the largest to date and has lower overlaps with current pretraining corpora.

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Chandan Gautam, Sethupathy Parameswaran, Aditya Kane, Yuan Fang, Savitha Ramasamy, Suresh Sundaram, Sunil Sahu, Xiaoli Li

Peering Behind the Shield: Guardrail Identification in Large Language Models (2026.findings-acl)

Challenge:	SCOOS leverages semantic cues embedded in class labels to improve classification accuracy.
Approach:	They propose a method to create a compact feature space around class label semantics . they use a shared latent space between ID features and class names to minimize losses .
Outcome:	The proposed method outperforms existing methods for out-of-scope intent detection and ID intent classification.

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Ziqing Yang, Yixin Wu, Rui Wen, Michael Backes, Yang Zhang

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

Challenge:	Identifying guardrails in conversational AI agents is critical for identifying malicious content . identifying guardrail components in black-box AI agents poses security challenges .
Approach:	They propose a method that leverages guard-specific adversarial prompts to detect guardrails in black-box AI agents.
Outcome:	The proposed method achieves perfect classification accuracy in multiple scenarios.

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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.

Extending LLMs to New Languages: A Case Study of Llama and Persian Adaptation (2025.coling-main)

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Samin Mahdizadeh Sani, Pouya Sadeghi, Thuy-Trang Vu, Yadollah Yaghoobzadeh, Gholamreza Haffari

Enhancing Interpretable Image Classification Through LLM Agents and Conditional Concept Bottleneck Models (2025.acl-long)

Challenge:	Large language models (LLMs) are mainly trained on English data and struggle with low-resource languages.
Approach:	They propose to add a new language to Llama to improve classification accuracy for Persian tasks by aligning representations through bilingual pretraining and instruction datasets.
Outcome:	The proposed model performs on generation and classification tasks with no adverse impact and sometimes even improvements on English tasks.

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Yiwen Jiang, Deval Mehta, Wei Feng, Zongyuan Ge

SINCon: Mitigate LLM-Generated Malicious Message Injection Attack for Rumor Detection (2025.acl-long)

Challenge:	Concept Bottleneck Models (CBMs) map visual representations to a set of humanunderstandable textual concepts, which are then interpreted by a linear combination of these concept scores.
Approach:	They propose a dynamic, agent-based approach that adjusts the concept bank in response to environmental feedback, optimizing the number of concepts for sufficiency yet concise coverage.
Outcome:	The proposed model improves classification accuracy by 6% and interpretability assessments by 30%.

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Mingqing Zhang, Qiang Liu, Xiang Tao, Shu Wu, Liang Wang

Robustness in NLP

Challenge:	Existing methods define important nodes as important and target them for attacks if the model treats nodes’ predictive influence more uniformly . Existing approaches target high predictive influence nodes but are vulnerable to malicious message injection attacks.
Approach:	They propose a defense mechanism that encourages the model to learn graph representations where nodes with varying importance have a more uniform influence on predictions.
Outcome:	Extensive experiments on the Twitter and Weibo datasets show that similarizing the predictive Influence of nodes with Contrastive Learning significantly enhances resistance against LLM-driven message injection attacks.

Logits Reranking via Semantic Labels for Hard Samples in Text Classification (2024.findings-emnlp)

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Peijie Huang, Junbao Huang, Yuhong Xu, Weizhen Li, Xisheng Xiao

Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification (2021.emnlp-main)

Challenge:	Existing research on text classification models ignores the semantic information inherent in labels, treating them as one-hot vectors.
Approach:	They propose a model-agnostic method that leverages label semantics and auto detection of hard samples to improve classification accuracy.
Outcome:	The proposed method shows significant improvements across different PLMs.

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Shuhuai Ren, Jinchao Zhang, Lei Li, Xu Sun, Jie Zhou

N24News: A New Dataset for Multimodal News Classification (2022.lrec-1)

Challenge:	Data augmentation aims to alleviate the overfitting issue in low-resource or class-imbalanced situations.
Approach:	They propose a framework called Text AutoAugment to enhance training samples . they use a Bayesian optimization algorithm to search for the best policy .
Outcome:	The proposed framework outperforms baseline methods on six benchmark datasets.

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Zhen Wang, Xu Shan, Xiangxie Zhang, Jie Yang

Caption Enriched Samples for Improving Hateful Memes Detection (2021.emnlp-main)

Challenge:	Current news datasets focus on text features and rarely leverage the feature of images.
Approach:	They propose a news dataset that uses both images and text to achieve better news classification.
Outcome:	The proposed model improves on the existing dataset N24News with text and image information.

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Efrat Blaier, Itzik Malkiel, Lior Wolf

WISE: Weak-Supervision-Guided Step-by-Step Explanations for Multimodal LLMs in Image Classification (2025.emnlp-main)

Challenge:	Existing methods for classifying memes are difficult to perform, with human accuracy only about 85% . recent state-of-the-art models perform considerably less accurately, achieving up to 64.73% accuracy.
Approach:	They propose to use an off-the-shelf caption generator to capture the first image and overlayed text.
Outcome:	The proposed tool improves classification accuracy for unimodal and multimodal models . the proposed tool can be used to model the contrast between image content and overlayed text .

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Yiwen Jiang, Deval Mehta, Siyuan Yan, Yaling Shen, Zimu Wang, Zongyuan Ge

The better your Syntax, the better your Semantics? Probing Pretrained Language Models for the English Comparative Correlative (2022.emnlp-main)

Challenge:	Existing MCoT methods focus on inter-object reasoning, overlooking intra-object understanding crucial for image classification.
Approach:	They propose a Weak-supervision-guided Step-by-step Explanation method that reformulates MCoTs under weak supervision into concise, interpretable reasoning chains.
Outcome:	The proposed method improves interpretability by 37% and improves classification accuracy.

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Leonie Weissweiler, Valentin Hofmann, Abdullatif Köksal, Hinrich Schütze

MULTIGUARD: An Efficient Approach for AI Safety Moderation Across Languages and Modalities (2025.emnlp-main)

Challenge:	Construction Grammar posits constructions as the central building blocks of language . human-like performance of pretrained language models on many NLP tasks has been alleged .
Approach:	They propose to use construction grammar to posit constructions as the central building blocks of language . they conduct experiments with three pretrained language models to examine their ability to classify and understand English comparative correlative .
Outcome:	The proposed models are able to recognise the English comparative correlative (CC) but fail to use its meaning.

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Sahil Verma, Keegan Hines, Jeff Bilmes, Charlotte Siska, Luke Zettlemoyer, Hila Gonen, Chandan Singh

Pushing on Text Readability Assessment: A Transformer Meets Handcrafted Linguistic Features (2021.emnlp-main)

Challenge:	Existing approaches to detect harmful queries to large language models are fallible and vulnerable to attacks that exploit mismatched generalization of model capabilities.
Approach:	They propose an approach to detect harmful queries to large language models (LLMs) OMNIGUARD identifies internal representations of an LLM/MLLM that are aligned across languages or modalities and builds a language-agnostic or modality-adic classifier for detecting harmful prompts.
Outcome:	OMNIGUARD improves harmful prompt classification accuracy by 11.57% over the strongest baseline in a multilingual setting, by 20.44% for image-based prompts, and sets a new SOTA for audio-based ones.

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Bruce W. Lee, Yoo Sung Jang, Jason Lee

Enhancing Neural Topic Model with Multi-Level Supervisions from Seed Words (2023.findings-acl)

Challenge:	ML models with handcrafted features are linguistically explainable, expandable, and competent against the modern neural models.
Approach:	They propose to combine traditional ML models with ML transformers to improve readability assessment by 99% accuracy.
Outcome:	The proposed model achieves state-of-the-art (SOTA) accuracy on popular datasets.

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Yang Lin, Xin Gao, Xu Chu, Yasha Wang, Junfeng Zhao, Chao Chen

MULTICOLLAB: A Multimodal Corpus of Dialogues for Analyzing Collaboration and Frustration in Language (2024.lrec-main)

Challenge:	Existing topic seed words are difficult to incorporate into topic models due to the semantic diversity of natural language.
Approach:	They propose a neural topic model enhanced with supervisions from seed words on word and document levels.
Outcome:	The proposed model outperforms the state-of-the-art seeded topic models in terms of topic quality and classification accuracy.

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Michael Peechatt, Cecilia Ovesdotter Alm, Reynold Bailey

Navigating Prompt Complexity for Zero-Shot Classification: A Study of Large Language Models in Computational Social Science (2024.lrec-main)

Challenge:	Existing methods to study complex emotions when a speaker collaborates with a partner are limited.
Approach:	They propose to fuse a multimodal dialogue resource with transcribed speech and eye gaze data to create a highly multimodal corpus.
Outcome:	The proposed model improves classification accuracy by 21% over baseline using sensor and speech data in 4.5 seconds.

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Yida Mu, Ben P. Wu, William Thorne, Ambrose Robinson, Nikolaos Aletras, Carolina Scarton, Kalina Bontcheva, Xingyi Song

Reassessing Semantic Knowledge Encoded in Large Language Models through the Word-in-Context Task (2024.lrec-main)

Challenge:	Existing instruction-tuned Large Language Models (LLMs) have impressive language understanding and the capacity to generate responses that follow specific prompts.
Approach:	They evaluate the zero-shot performance of two publicly accessible LLMs, ChatGPT and OpenAssistant, in the context of six Computational Social Science classification tasks.
Outcome:	The proposed LLMs perform better than state-of-the-art models on social science tasks.

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Yoshihiko Hayashi

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

Challenge:	Recent advances in large language models (LLMs) have propelled significant progress, extending their application across various domains including dialogue systems, text generation, translation systems, and beyond.
Approach:	They propose to use the Word-in-Context (WiC) task to reassess the semantic knowledge encoded in large language models (LLMs) they prompt LLMs to generate natural language descriptions that contrast the meanings of the target word in two contextual sentences given in the WiC dataset.
Outcome:	The proposed model significantly improves the classification accuracy of the two models.

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

Speech Analysis of Language Varieties in Italy (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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Moreno La Quatra, Alkis Koudounas, Elena Baralis, Sabato Marco Siniscalchi

LETS-C: Leveraging Text Embedding for Time Series Classification (2025.acl-long)

Challenge:	Recent advances in self-supervised learning provide new opportunities to analyze Italy’s linguistic varieties using speech data alone.
Approach:	They propose to automatically identify the geographic region of origin of speech samples drawn from Italy's diverse language varieties.
Outcome:	The proposed model can identify regions from speech recording and improve classification accuracy and yields embeddings that distinctly separate regional varieties.

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Rachneet Kaur, Zhen Zeng, Tucker Balch, Manuela Veloso

Data Descriptions from Large Language Models with Influence Estimation (2025.emnlp-main)

Challenge:	Recent advances in language modeling have shown promising results when applied to time series data.
Approach:	They propose a method to fine-tune large language models for time series classification tasks using text embedding models and a simple classification head.
Outcome:	The proposed model outperforms the current SOTA model on a time series classification benchmark and uses only 14.5% of the trainable parameters.

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Chaeri Kim, Jaeyeon Bae, Taehwan Kim