2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDHLT-3.2
Paper Title MULTI PATH TRAINING FRAMEWORK FOR DATA-DRIVEN OPEN-DOMAIN CONVERSATION SYSTEM
Authors Sixing Wu, Dawei Zhang, Ying Li, Zhonghai Wu, Peking University, China
SessionHLT-3: Dialogue Systems 1: General Topics
LocationGather.Town
Session Time:Tuesday, 08 June, 14:00 - 14:45
Presentation Time:Tuesday, 08 June, 14:00 - 14:45
Presentation Poster
Topic Human Language Technology: [HLT-DIAL] Discourse and Dialog
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Nowadays, web data is often used to train a dialogue system. However, noises in web data can disturb the training process, as well as can impact the performance. Consequently, dialogue models tend to be brittle when receiving noisy inputs during the inference. This paper proposes a novel framework, Multi-Path Training (MPT), for training a robust dialogue response generation system. MPT improves the robustness to the noisy training data and the noisy inference queries using three paths. Experimental results show MPT can outperform baselines using the same backbone model, and also prove MPT can improve the robustness to the noise in both the training and inference stage.