Evaluating Discourse Cohesion in Pre-trained Language Models

Jie He; Wanqiu Long; Deyi Xiong

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arXiv · 2503.06137

Evaluating Discourse Cohesion in Pre-trained Language Models

Jie He·Wanqiu Long·Deyi Xiong

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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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Jie He, Wanqiu Long, Deyi Xiong. 2025-03-08. Evaluating Discourse Cohesion in Pre-trained Language Models. https://arxiv.org/abs/2503.06137

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