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

9 Jan 2019  ·  Qian Chen, Wen Wang ·

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.

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Conversational Response Selection Advising Corpus CtxDec & -Rev R@1 31.0 # 1
R@10 78.8 # 1
R@50 97.8 # 1
Conversational Response Selection DSTC7 Ubuntu Sequential Attention-based Network 1-of-100 Accuracy 64.5% # 4
Conversational Response Selection Ubuntu Dialogue (v1, Ranking) ESIM R10@1 0.796 # 13
R10@2 0.894 # 13
R10@5 0.975 # 13


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