Papers with Modeling
Identifying Informational Sources in News Articles (2023.emnlp-main)
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| Challenge: | Identifying sources of information in news articles is relevant to many tasks in NLP, including misinformation detection and argumentation. |
| Approach: | They propose a task to study compositionality of sources in news articles to understand how they are chosen to complement each other. |
| Outcome: | The proposed dataset can be used to train high-performing models for information detection and source attribution. |
Modeling Factual Claims with Semantic Frames (2020.lrec-1)
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| Challenge: | In recent years, the proliferation of misinformation has reached a staggering pace eroding people's confidence in politics and even affected democracies. |
| Approach: | They propose an extension of the Berkeley FrameNet for the structured and semantic modeling of factual claims. |
| Outcome: | The proposed extension provides 2,540 fully annotated sentences and can be used to understand how these frames are intended to work and to train machine learning models. |
Let’s Make Your Request More Persuasive: Modeling Persuasive Strategies via Semi-Supervised Neural Nets on Crowdfunding Platforms (N19-1)
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| Challenge: | Existing models can't quantify persuasiveness of requests or extract successful persuasive strategies. |
| Approach: | They propose a semi-supervised hierarchical neural network model to quantify persuasiveness and identify persuasive strategies in advocacy requests. |
| Outcome: | The proposed method outperforms baseline models and offers increased interpretability of persuasive speech. |
Modeling Layout Reading Order as Ordering Relations for Visually-rich Document Understanding (2024.emnlp-main)
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Chong Zhang, Yi Tu, Yixi Zhao, Chenshu Yuan, Huan Chen, Yue Zhang, Mingxu Chai, Ya Guo, Huijia Zhu, Qi Zhang, Tao Gui
| Challenge: | Existing models of layout reading order do not convey the complete reading order information in the layout. |
| Approach: | They propose to model layout reading order as ordering relations over layout elements . they propose a reading-order-relation-enhancing pipeline to improve model performance . |
| Outcome: | The proposed model outperforms existing models on a visual-rich document dataset and on eight cross-domain VrD-IE/QA tasks without targeted optimization. |
Humans Hallucinate Too: Language Models Identify and Correct Subjective Annotation Errors With Label-in-a-Haystack Prompts (2025.emnlp-main)
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Georgios Chochlakis, Peter Wu, Tikka Arjun Singh Bedi, Marcus Ma, Kristina Lerman, Shrikanth Narayanan
| Challenge: | Existing approaches to model complex subjective tasks in natural language are limited by significant variation in annotations. |
| Approach: | They propose a simple in-context learning binary filtering baseline that estimates the reasonableness of a document-label pair. |
| Outcome: | The proposed approach can be integrated into annotation pipelines to enhance signal-to-noise ratios. |