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Transformer has been successfully applied to many natural language processingtasks. However, for textual sequence matching, simple matching between therepresentation of a pair of sequences might bring in unnecessary noise. In thispaper, we propose a new approach to sequence pair matching with Transformer, bylearning head-wise matching representations on multiple levels. Experimentsshow that our proposed approach can achieve new state-of-the-art performance onmultiple tasks that rely only on pre-computed sequence-vector-representation,such as SNLI, MNLI-match, MNLI-mismatch, QQP, and SQuAD-binary.

Multi-level Head-wise Match and Aggregation in Transformer for Textual Sequence Matching