The noetic end-to-end response selection challenge as one track in Dialog
System Technology Challenges 7 (DSTC7) aims to push the state of the art of
utterance classification for real world goal-oriented dialog systems, for which
participants need to select the correct next utterances from a set of
candidates for the multi-turn context. This paper describes our systems that
are ranked the top on both datasets under this challenge, one focused and small
(Advising) and the other more diverse and large (Ubuntu). Previous
state-of-the-art models use hierarchy-based (utterance-level and token-level)
neural networks to explicitly model the interactions among different turns'
utterances for context modeling. In this paper, we investigate a sequential
matching model based only on chain sequence for multi-turn response selection.
Our results demonstrate that the potentials of sequential matching approaches
have not yet been fully exploited in the past for multi-turn response
selection. In addition to ranking the top in the challenge, the proposed model
outperforms all previous models, including state-of-the-art hierarchy-based
models, and achieves new state-of-the-art performances on two large-scale
public multi-turn response selection benchmark datasets.

本文提出了一种基于序列匹配模型的多轮对话响应选择算法，相比之前的模型，该模型在提升多轮对话系统中的响应选择效果方面取得了更好的表现，并在两个大规模公开数据集上均获得了最优表现。