Publication: Towards Unsupervised Recognition of Token-level Semantic Differences in Related Documents
Towards Unsupervised Recognition of Token-level Semantic Differences in Related Documents
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Vamvas, J., & Sennrich, R. (2023). Towards Unsupervised Recognition of Token-level Semantic Differences in Related Documents. Proceedings of the Conference on Empirical Methods in Natural Language Processing, 13543–13552. https://doi.org/10.18653/v1/2023.emnlp-main.835
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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
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Vamvas, J., & Sennrich, R. (2023). Towards Unsupervised Recognition of Token-level Semantic Differences in Related Documents. Proceedings of the Conference on Empirical Methods in Natural Language Processing, 13543–13552. https://doi.org/10.18653/v1/2023.emnlp-main.835
