Papers by Zhenyun Deng
Document-level Claim Extraction and Decontextualisation for Fact-Checking (2024.acl-long)
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| Challenge: | Existing methods for document-level claim extraction focus on identifying and extracting claims from individual sentences. |
| Approach: | They propose a method for document-level claim extraction for fact-checking which aims to extract check-worthy claims from documents and decontextualise them so they can be understood out of context. |
| Outcome: | The proposed method extracts check-worthy claims from documents and decontextualises them so they can be understood out of context. |
TaKG: A New Dataset for Paragraph-level Table-to-Text Generation Enhanced with Knowledge Graphs (2022.findings-aacl)
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| Challenge: | Existing table-to-text generation benchmarks have some limitations, such as E2E and ToTTo focusing on singlesentence generation tasks. |
| Approach: | They propose a new table-to-text generation dataset called TaKG that uses a set of knowledge graphs to enhance table input. |
| Outcome: | The proposed model outperforms existing models for short-text generation tasks and shows reliable performance on long-text generated across a variety of metrics. |
Improving Zero-shot Sentence Decontextualisation with Content Selection and Planning (2025.emnlp-main)
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| Challenge: | Existing methods for extracting sentences from documents leave some implicit discourse information in the sentence unresolved due to their lack of context. |
| Approach: | They propose a content selection framework for zero-shot decontextualisation which determines what content should be mentioned and in what order for a sentence to be understood out of context. |
| Outcome: | The proposed framework outperforms existing methods in rewriting sentences that lack context while maintaining original meaning. |
Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning (2024.findings-acl)
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Qiming Bao, Alex Peng, Zhenyun Deng, Wanjun Zhong, Gael Gendron, Timothy Pistotti, Neset Tan, Nathan Young, Yang Chen, Yonghua Zhu, Paul Denny, Michael Witbrock, Jiamou Liu
| Challenge: | Empirical evidence shows that our proposed method improves performance across seven downstream tasks. |
| Approach: | They propose a logic-driven data augmentation approach that converts text into AMR graphs and converts them back into text to create augmented data. |
| Outcome: | The proposed method leads on the ReClor leaderboard and improves on seven downstream tasks. |
PledgeTracker: A System for Monitoring the Fulfilment of Pledges (2025.emnlp-demos)
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Yulong Chen, Michael Sejr Schlichtkrull, Zhenyun Deng, David Corney, Nasim Asl, Joshua Salisbury, Andrew Dudfield, Andreas Vlachos
| Challenge: | Existing methods simplify pledge verification into document classification task, overlooking its dynamic temporal and multi-document nature. |
| Approach: | They propose a system that reformulates pledge verification into structured event timeline construction. |
| Outcome: | The proposed system shows that it can be used in real-world workflows and reduces human verification effort. |
Prompt-based Conservation Learning for Multi-hop Question Answering (2022.coling-1)
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| Challenge: | Existing multi-hop QA methods fail to answer a large fraction of sub-questions even if their parent questions are answered correctly. |
| Approach: | They propose a Prompt-based Conservation Learning framework that acquires new knowledge from multi-hop QA tasks while conserving old knowledge learned on single-hop tasks. |
| Outcome: | The proposed framework acquires new knowledge from multi-hop QA tasks while conserving old knowledge learned on single-hop tasks, mitigating forgetting. |