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Context modeling has a pivotal role in open domain conversation. Existingworks either use heuristic methods or jointly learn context modeling andresponse generation with an encoder-decoder framework. This paper proposes anexplicit context rewriting method, which rewrites the last utterance byconsidering context history. We leverage pseudo-parallel data and elaborate acontext rewriting network, which is built upon the CopyNet with thereinforcement learning method. The rewritten utterance is beneficial tocandidate retrieval, explainable context modeling, as well as enabling toemploy a single-turn framework to the multi-turn scenario. The empiricalresults show that our model outperforms baselines in terms of the rewritingquality, the multi-turn response generation, and the end-to-end retrieval-basedchatbots.

Unsupervised Context Rewriting for Open Domain Conversation