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The noetic end-to-end response selection challenge as one track in DialogSystem Technology Challenges 7 (DSTC7) aims to push the state of the art ofutterance classification for real world goal-oriented dialog systems, for whichparticipants need to select the correct next utterances from a set ofcandidates for the multi-turn context. This paper describes our systems thatare ranked the top on both datasets under this challenge, one focused and small(Advising) and the other more diverse and large (Ubuntu). Previousstate-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 sequentialmatching model based only on chain sequence for multi-turn response selection.Our results demonstrate that the potentials of sequential matching approacheshave not yet been fully exploited in the past for multi-turn responseselection. In addition to ranking the top in the challenge, the proposed modeloutperforms all previous models, including state-of-the-art hierarchy-basedmodels, and achieves new state-of-the-art performances on two large-scalepublic multi-turn response selection benchmark datasets.

Sequential Attention-based Network for Noetic End-to-End Response Selection