  The noetic end-to-end response selection challenge as one track in the 7th
Dialog System Technology Challenges (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 presents our systems that are
ranked top 1 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 top 1 in the challenge, the proposed model
outperforms all previous models, including state-of-the-art hierarchy-based
models, on two large-scale public multi-turn response selection benchmark
datasets.
