  In multi-turn dialogue generation, response is usually related with only a
few contexts. Therefore, an ideal model should be able to detect these relevant
contexts and produce a suitable response accordingly. However, the widely used
hierarchical recurrent encoderdecoder models just treat all the contexts
indiscriminately, which may hurt the following response generation process.
Some researchers try to use the cosine similarity or the traditional attention
mechanism to find the relevant contexts, but they suffer from either
insufficient relevance assumption or position bias problem. In this paper, we
propose a new model, named ReCoSa, to tackle this problem. Firstly, a word
level LSTM encoder is conducted to obtain the initial representation of each
context. Then, the self-attention mechanism is utilized to update both the
context and masked response representation. Finally, the attention weights
between each context and response representations are computed and used in the
further decoding process. Experimental results on both Chinese customer
services dataset and English Ubuntu dialogue dataset show that ReCoSa
significantly outperforms baseline models, in terms of both metric-based and
human evaluations. Further analysis on attention shows that the detected
relevant contexts by ReCoSa are highly coherent with human's understanding,
validating the correctness and interpretability of ReCoSa.
