论文标题
文本序列匹配的变压器中的多级头明角匹配和聚合
Multi-level Head-wise Match and Aggregation in Transformer for Textual Sequence Matching
论文作者
论文摘要
变压器已成功应用于许多自然语言处理任务。但是，对于文本序列匹配，一对序列的表示之间的简单匹配可能会带来不必要的噪声。在本文中，我们通过学习多个级别的头明匹配表示，提出了一种与变压器匹配的新方法，以序列对匹配。实验表明，我们提出的方法可以在多个任务上实现新的最新性能，这些任务仅依赖于预先计算的序列 - 矢量代理，例如SNLI，MNLI匹配，MNLI匹配，MNLI - 匹配，QQP和Squad-Binary。
Transformer has been successfully applied to many natural language processing tasks. However, for textual sequence matching, simple matching between the representation of a pair of sequences might bring in unnecessary noise. In this paper, we propose a new approach to sequence pair matching with Transformer, by learning head-wise matching representations on multiple levels. Experiments show that our proposed approach can achieve new state-of-the-art performance on multiple tasks that rely only on pre-computed sequence-vector-representation, such as SNLI, MNLI-match, MNLI-mismatch, QQP, and SQuAD-binary.
