Document-level Event Factuality Identification using ChatGPT via Cross-Lingual and Syntactic Data Augmentation | IEEE Conference Publication | IEEE Xplore

Document-level Event Factuality Identification using ChatGPT via Cross-Lingual and Syntactic Data Augmentation


Abstract:

Event Factuality Identification (EFI) aims to assess the factual degree of events in texts, which is crucial and fundamental for many downstream tasks of Natural Language...Show More

Abstract:

Event Factuality Identification (EFI) aims to assess the factual degree of events in texts, which is crucial and fundamental for many downstream tasks of Natural Language Processing (NLP). Document-level Event Factuality Identification (DEFI), as a branch of EFI tasks that focuses on document text, is an important task in NLP. Currently, research on DEFI is often viewed as a supervised classification task which relies on annotated information. For most datasets of DEFI, the sparsity of the data and the uneven distribution of corpora with different labels limit the research progress of the DEFI task. With the emergence of ChatGPT, it has become feasible for data augmentation by using ChatGPT, which is more accurate and fully functional than all previous tools. This precisely helps to address the problems that most datasets on DEFI have, and we can use ChatGPT to improve and expand the dataset. This paper outlines a systematic framework model, DEFI-G, which leverages Large Language Model (LLM) to process materials reported in the document, greatly expanding the original materials. Moreover, based on GAT, we change the connection method between nodes to improve their performance, resulting in improved task accuracy. The results shows that DEFI-G performs better than all previous baselines.
Date of Conference: 30 June 2024 - 05 July 2024
Date Added to IEEE Xplore: 09 September 2024
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Conference Location: Yokohama, Japan

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