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Despite the success of neural dialogue systems in achieving high performanceon the leader-board, they cannot meet users' requirements in practice, due totheir poor reasoning skills. The underlying reason is that most neural dialoguemodels only capture the syntactic and semantic information, but fail to modelthe logical consistency between the dialogue history and the generatedresponse. Recently, a new multi-turn dialogue reasoning task has been proposed,to facilitate dialogue reasoning research. However, this task is challenging,because there are only slight differences between the illogical response andthe dialogue history. How to effectively solve this challenge is still worthexploring. This paper proposes a Fine-grained Comparison Model (FCM) to tacklethis problem. Inspired by human's behavior in reading comprehension, acomparison mechanism is proposed to focus on the fine-grained differences inthe representation of each response candidate. Specifically, each candidaterepresentation is compared with the whole history to obtain a historyconsistency representation. Furthermore, the consistency signals between eachcandidate and the speaker's own history are considered to drive a model toprefer a candidate that is logically consistent with the speaker's historylogic. Finally, the above consistency representations are employed to output aranking list of the candidate responses for multi-turn dialogue reasoning.Experimental results on two public dialogue datasets show that our methodobtains higher ranking scores than the baseline models.

FCM: A Fine-grained Comparison Model for Multi-turn Dialogue Reasoning