Towards Unsupervised Recognition of Token-level Semantic Differences in Related Documents

• URL: http://arxiv.org/abs/2305.13303v3
• Date: Fri, 20 Oct 2023 12:27:41 GMT
• Title: Towards Unsupervised Recognition of Token-level Semantic Differences in Related Documents
• Authors: Jannis Vamvas and Rico Sennrich
• Abstract summary: We formulate recognizing semantic differences as a token-level regression task. We study three unsupervised approaches that rely on a masked language model. Our results show that an approach based on word alignment and sentence-level contrastive learning has a robust correlation to gold labels.
• Score: 61.63208012250885
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatically highlighting words that cause semantic differences between two documents could be useful for a wide range of applications. We formulate recognizing semantic differences (RSD) as a token-level regression task and study three unsupervised approaches that rely on a masked language model. To assess the approaches, we begin with basic English sentences and gradually move to more complex, cross-lingual document pairs. Our results show that an approach based on word alignment and sentence-level contrastive learning has a robust correlation to gold labels. However, all unsupervised approaches still leave a large margin of improvement. Code to reproduce our experiments is available at https://github.com/ZurichNLP/recognizing-semantic-differences

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