Text Segmentation by Cross Segment Attention
Michal Lukasik | Boris Dadachev | Kishore Papineni | Gonçalo Simões
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
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.