Papers with detection task
Evaluating Document Coherence Modeling (2021.tacl-1)
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| Challenge: | a new study examines pretrained language models' ability to model discourse and pragmatic phenomena. |
| Approach: | They propose a sentence intrusion detection task using a dictionary dataset . they show that pretrained LMs perform impressively in in-domain evaluation . |
| Outcome: | The proposed dataset shows that pretrained LMs perform impressively in in-domain evaluation, but experience a substantial drop in the cross-domain setting, indicating limited generalization capacity. |
Dialogue Act-based Breakdown Detection in Negotiation Dialogues (2021.eacl-main)
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| Challenge: | Recent studies have succeeded in modeling a negotiating agent in natural language that can control both text generation and reasoning in goal-oriented dialogue systems. |
| Approach: | They propose a human-human negotiation dialogue dataset that features increased complexities in terms of the number of possible solutions and a utility function. |
| Outcome: | The proposed method performs comparable to text-based approaches in existing corpora and better results in the proposed dataset. |
Identifying Bias in Machine-generated Text Detection (2026.acl-long)
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| Challenge: | a growing number of generative AI systems are detecting text generated by a model or written by . humans perform poorly at the detection task, but show no significant biases on the studied attributes. |
| Approach: | They examine gender, race/ethnicity, English-language learner status, and economic status . they find several models tend to classify disadvantaged groups as machine-generated . |
| Outcome: | The proposed models show strong performance but can cause negative impacts . the models classify disadvantaged groups as machine-generated, while economically disadvantaged students' essays are less likely to be classified as machine generated . |
A Multi-Task Learning Framework for Multi-Target Stance Detection (2021.findings-acl)
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| Challenge: | Existing models fail to learn target-specific representations and are prone to overfitting. |
| Approach: | They propose a multi-task learning network to train one model on all target pairs . their results show that their proposed model outperforms the best-performing baseline by 12.39% . |
| Outcome: | The proposed model outperforms the best-performing baseline model by 12.39% in macro-averaged F1-score. |
ImaRA: An Imaginative Frame Augmented Method for Low-Resource Multimodal Metaphor Detection and Explanation (2025.findings-naacl)
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| Challenge: | Existing methods for multimodal metaphor detection neglect cross-domain and attribute similarity characteristics underlying multimodal understanding. |
| Approach: | They propose an Imaginative FRame Augmented method for multimodal metaphor detection and explanation . they use a cross-modal imagination dataset rich in multimodal multimodal expressions . |
| Outcome: | The proposed method outperforms existing methods with training data on two datasets. |
ToXCL: A Unified Framework for Toxic Speech Detection and Explanation (2024.naacl-long)
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| Challenge: | Existing models that focus on explicit toxic speech detection and explanation are prone to error propagation problems . et al., 2018) show that toxic speech models can be prone for generating errors . |
| Approach: | They propose a framework that can detect and explain toxic speech using a target group generator and an encoder-decoder model. |
| Outcome: | The proposed model outperforms baseline models and achieves state-of-the-art effectiveness . the proposed model generates a toxic explanation that matches the ground truth explanation . |
Telling a Lie: Analyzing the Language of Information and Misinformation during Global Health Events (2022.lrec-1)
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| Challenge: | a new dataset is available to stimulate research on health misinformation . linguistic characteristics of health misinfonia are unique to COVID-19 and other events . |
| Approach: | They propose a new dataset that analyzes health misinformation at scale . it includes 2.8 million news articles and social media posts covering diseases . authors propose an annotation framework that allows for strong agreement between annotators . |
| Outcome: | The proposed dataset is based on 2.8 million news articles and social media posts spanning 1900s to present . it shows that the proposed model is robust and can be used to detect misinformation . |
Build it Break it Fix it for Dialogue Safety: Robustness from Adversarial Human Attack (D19-1)
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| Challenge: | Detecting offensive language in the context of a dialogue is an increasingly important application of natural language processing. |
| Approach: | They propose to train a model to be robust to such attacks by iterative build it, break it, fix it scheme with humans and models in the loop. |
| Outcome: | The proposed model is significantly more robust to such human attacks than previous systems. |
Personal Information Leakage Detection in Conversations (2020.emnlp-main)
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| Challenge: | 4.5 billion dollars will be invested in conversational assistants (chatbots) by 2021, according to Opus Research 2 . Among diverse types of chatbots, Google Duplex represents the kind of AI personal assistants that act on behalf of people to perform simple tasks. |
| Approach: | They propose to protect personal information by warning users of detected suspicious sentences . they propose to use a constrained alignment problem to perform an alignment optimization problem . |
| Outcome: | The proposed models outperform baseline models on the behavior of personalized chit-chat dialogue systems. |
HateGAN: Adversarial Generative-Based Data Augmentation for Hate Speech Detection (2020.coling-main)
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| 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. |
Words Like Knives: Backstory-Personalized Modeling and Detection of Violent Communication (2025.emnlp-main)
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| Challenge: | a recent study examines the role of personalization in enabling LLMs to serve as effective mediators in human communication for authentic connection. |
| Approach: | They leverage nonviolent communication theory to evaluate LLMs in detecting conversational breakdowns . they annotate a subset of dialogues and obtain fine-grained labels of communication breakdown types . |
| Outcome: | The proposed dataset analyzes human interactions and relationships in a human context. |
Media Attitude Detection via Framing Analysis with Events and their Relations (2024.emnlp-main)
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| Challenge: | a recent study examined the effects of media framing on public perception and understanding of news articles. |
| Approach: | They propose to extract framing devices employed by media to assess their role in framating the narrative. |
| Outcome: | The proposed method surpasses baseline models and offers a more detailed and explainable analysis of media framing effects. |
Breaking the "Provable Security": Detecting Finite-Precision Artifacts in LLM-based Steganography via Low-Probability Vanishing (2026.findings-acl)
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| Challenge: | Recent advances in Large Language Models have fostered a new class of generative linguistic steganography, claim “provably secure” by theoretically aligning the stego distribution with the language model’s natural distribution. |
| Approach: | They propose a framework that transforms the detection task from semantic classification to a statistical audit of the sampling mechanism. |
| Outcome: | The proposed framework breaks the security of AC and Meteor with high detection accuracy, whereas state-of-the-art semantic steganalyzers degrade to random guessing. |
Encoding Spreadsheets for Large Language Models (2024.emnlp-main)
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Haoyu Dong, Jianbo Zhao, Yuzhang Tian, Junyu Xiong, Mengyu Zhou, Yun Lin, José Cambronero, Yeye He, Shi Han, Dongmei Zhang
| Challenge: | Spreadsheets are characterized by their extensive two-dimensional grids, flexible layouts, and varied formatting options, which pose significant challenges for large language models (LLMs). |
| Approach: | They propose a structural-anchor-based compression, inverse index translation, and data-format-aware aggregation module to compress spreadsheets effectively. |
| Outcome: | The proposed method outperforms the existing model in GPT4 and achieves a state-of-the-art 78.9% F1 score. |