HSAN: A Hierarchical Self-Attention Network for Multi-Turn Dialogue Generation

In the multi-turn dialogue system, response generation is not only related to the sentences in context but also relies on the words in each utterance. Although there are lots of methods that pay attention to model words and utterances, there still exist problems such as tending to generate common responses. In this paper, we propose a hierarchical self-attention network, named HSAN, which attends to the important words and utterances in context simultaneously. Firstly, we use the hierarchical encoder to update the word and utterance representations with their position information respectively. Secondly, the response representations are updated by the mask self-attention module in the decoder. Finally, the relevance between utterances and response is computed by another self-attention module and used for the next response decoding process. In terms of automatic metrics and human judgements, experimental results show that HSAN significantly outperforms all baselines on two common public datasets.