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Zhenyun Deng

7 accepted papers

2025

Improving Zero-shot Sentence Decontextualisation with Content Selection and Planning

EMNLP 2025

Extracting individual sentences from a document as evidence or reasoning steps is commonly done in many NLP tasks. However, extracted sentences often lack context necessary to make them understood, e.g., coreference and background information. To this end, we propose a content selection and planning

2024

Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning

ACL 2024findings

Combining large language models with logical reasoning enhances their capacity to address problems in a robust and reliable manner. Nevertheless, the intricate nature of logical reasoning poses challenges when gathering reliable data from the web to build comprehensive training datasets, subsequentl…

2024

Document-level Claim Extraction and Decontextualisation for Fact-Checking

ACL 2024long

Selecting which claims to check is a time-consuming task for human fact-checkers, especially from documents consisting of multiple sentences and containing multiple claims. However, existing claim extraction approaches focus more on identifying and extracting claims from individual sentences, e.g.,…

2024

Robust Node Classification on Graph Data with Graph and Label Noise

AAAI 2024technical

Current research for node classification focuses on dealing with either graph noise or label noise, but few studies consider both of them. In this paper, we propose a new robust node classification method to simultaneously deal with graph noise and label noise. To do this, we design a graph contrast…

2022

Interpretable AMR-Based Question Decomposition for Multi-hop Question Answering

IJCAI 2022poster

Effective multi-hop question answering (QA) requires reasoning over multiple scattered paragraphs and providing explanations for answers. Most existing approaches cannot provide an interpretable reasoning process to illustrate how these models arrive at an answer. In this paper, we propose a Questio…

Cited by 27SourcePDFScholar
2022

Prompt-based Conservation Learning for Multi-hop Question Answering

COLING 2022main

Multi-hop question answering (QA) requires reasoning over multiple documents to answer a complex question and provide interpretable supporting evidence. However, providing supporting evidence is not enough to demonstrate that a model has performed the desired reasoning to reach the correct answer. M…

Cited by 4SourcePDFScholar