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Text Segmentation by Cross Segment Attention

2020-04-30EMNLP 2020Code Available1· sign in to hype

Michal Lukasik, Boris Dadachev, Gonçalo Simões, Kishore Papineni

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Abstract

Document and discourse segmentation are two fundamental NLP tasks pertaining to breaking up text into constituents, which are commonly used to help downstream tasks such as information retrieval or text summarization. In this work, we propose three transformer-based architectures and provide comprehensive comparisons with previously proposed approaches on three standard datasets. We establish a new state-of-the-art, reducing in particular the error rates by a large margin in all cases. We further analyze model sizes and find that we can build models with many fewer parameters while keeping good performance, thus facilitating real-world applications.

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