2 research outputs found
Sequential Attention-based Network for Noetic End-to-End Response Selection
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.Comment: Ranked first in DSTC7 Track 1. Accepted for an oral presentation at
the DSTC7 workshop at AAAI 2019. The source code is available no
Sequential Neural Networks for Noetic End-to-End Response Selection
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.Comment: 26 pages, 3 figures, Computer Speech & Language. arXiv admin note:
substantial text overlap with arXiv:1901.0260