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5 months ago

Pre-Trained and Attention-Based Neural Networks for Building Noetic Task-Oriented Dialogue Systems

Jia-Chen Gu; Tianda Li; Quan Liu; Xiaodan Zhu; Zhen-Hua Ling; Yu-Ping Ruan

Pre-Trained and Attention-Based Neural Networks for Building Noetic Task-Oriented Dialogue Systems

Abstract

The NOESIS II challenge, as the Track 2 of the 8th Dialogue System Technology Challenges (DSTC 8), is the extension of DSTC 7. This track incorporates new elements that are vital for the creation of a deployed task-oriented dialogue system. This paper describes our systems that are evaluated on all subtasks under this challenge. We study the problem of employing pre-trained attention-based network for multi-turn dialogue systems. Meanwhile, several adaptation methods are proposed to adapt the pre-trained language models for multi-turn dialogue systems, in order to keep the intrinsic property of dialogue systems. In the released evaluation results of Track 2 of DSTC 8, our proposed models ranked fourth in subtask 1, third in subtask 2, and first in subtask 3 and subtask 4 respectively.

Benchmarks

BenchmarkMethodologyMetrics
conversation-disentanglement-on-ircBERT + BiLSTM
F: 46.8
P: 44.3
R: 49.6
VI: 93.3

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Pre-Trained and Attention-Based Neural Networks for Building Noetic Task-Oriented Dialogue Systems | Papers | HyperAI