Evaluating Discourse Cohesion in Pre-trained Language Models

• URL: http://arxiv.org/abs/2503.06137v1
• Date: Sat, 08 Mar 2025 09:19:53 GMT
• Title: Evaluating Discourse Cohesion in Pre-trained Language Models
• Authors: Jie He, Wanqiu Long, Deyi Xiong,
• Abstract summary: We propose a test suite to evaluate the cohesive ability of pre-trained language models.<n>The test suite contains multiple cohesion phenomena between adjacent and non-adjacent sentences.
• Score: 42.63411207004852
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large pre-trained neural models have achieved remarkable success in natural language process (NLP), inspiring a growing body of research analyzing their ability from different aspects. In this paper, we propose a test suite to evaluate the cohesive ability of pre-trained language models. The test suite contains multiple cohesion phenomena between adjacent and non-adjacent sentences. We try to compare different pre-trained language models on these phenomena and analyze the experimental results,hoping more attention can be given to discourse cohesion in the future.

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