EACL 2024に論文が採択 Paper was accepted at EACL 2024

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Fact verification に関する論文が、自然言語処理分野における主要なカンファレンスである EACL2024 に採択されました。

本論文は、第一著者の向原悠太さんが、STAIR Lab のインターンとして取り組んだ研究です。向原さんが本発表に関するブログ記事を公開していますのでぜひご覧ください。

Rethinking Loss Functions for Fact Verification

Yuta Mukobara, Yutaro Shigeto, and Masashi Shimbo

We explore loss functions for fact verification in the FEVER shared task. While the cross-entropy loss is a standard objective for training verdict predictors, it fails to capture the heterogeneity among the FEVER verdict classes. In this paper, we develop two task-specific objectives tailored to FEVER. Experimental results confirm that the proposed objective functions outperform the standard cross-entropy. Performance is further improved when these objectives are combined with simple class weighting, which effectively overcomes the imbalance in the training data. The source code is available.

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