A Span Extraction Approach for Information Extraction on Visually-Rich Documents

Nguyen, Tuan-Anh; Vu, Hieu Minh; Sơn, Nguyễn Hồng; Nguyen, Minh-Tien

doi:10.1007/978-3-030-86159-9_25

Cited by 3 publications

(1 citation statement)

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“…As our QA models use the best answer span for each question, only one emotion, one cause, and one EC pair are predicted for each document which are appropriate for the ECPE dataset. We also aware that the prediction of spans should be multiple and we aim to address this limitation in future work by using multiple span extraction methods (Nguyen et al, 2021;Fu et al, 2021).…”

Section: Guided Emotion-cause Pair Extractionmentioning

confidence: 99%

Emotion-Cause Pair Extraction as Question Answering

Nguyen¹,

Nguyen²

2023

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The task of Emotion-Cause Pair Extraction (ECPE) aims to extract all potential emotioncause pairs of a document without any annotation of emotion or cause clauses. Previous approaches on ECPE have tried to improve conventional two-step processing schemes by using complex architectures for modeling emotion-cause interaction. In this paper, we cast the ECPE task to the question answering (QA) problem and propose simple yet effective BERT-based solutions to tackle it. Given a document, our Guided-QA model first predicts the best emotion clause using a fixed question. Then the predicted emotion is used as a question to predict the most potential cause for the emotion. We evaluate our model on a standard ECPE corpus. The experimental results show that despite its simplicity, our Guided-QA achieves promising results and is easy to reproduce. The code of Guided-QA is also provided.

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Section: Guided Emotion-cause Pair Extractionmentioning

confidence: 99%