Sequential Neural Networks for Noetic End-to-End Response Selection

• URL: http://arxiv.org/abs/2003.02126v1
• Date: Tue, 3 Mar 2020 04:36:33 GMT
• Title: Sequential Neural Networks for Noetic End-to-End Response Selection
• Authors: Qian Chen, Wen Wang
• Abstract summary: This paper presents our systems that are ranked top 1 on both datasets under this challenge. 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.
• Score: 4.996858281980058
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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