Pre-Training for Query Rewriting in A Spoken Language Understanding System

• URL: http://arxiv.org/abs/2002.05607v1
• Date: Thu, 13 Feb 2020 16:31:50 GMT
• Title: Pre-Training for Query Rewriting in A Spoken Language Understanding System
• Authors: Zheng Chen, Xing Fan, Yuan Ling, Lambert Mathias, Chenlei Guo
• Abstract summary: We first propose a neural-retrieval based approach for query rewriting. Then, inspired by the wide success of pre-trained contextual language embeddings, we propose a language-modeling (LM) based approach.
• Score: 14.902583546933563
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Query rewriting (QR) is an increasingly important technique to reduce customer friction caused by errors in a spoken language understanding pipeline, where the errors originate from various sources such as speech recognition errors, language understanding errors or entity resolution errors. In this work, we first propose a neural-retrieval based approach for query rewriting. Then, inspired by the wide success of pre-trained contextual language embeddings, and also as a way to compensate for insufficient QR training data, we propose a language-modeling (LM) based approach to pre-train query embeddings on historical user conversation data with a voice assistant. In addition, we propose to use the NLU hypotheses generated by the language understanding system to augment the pre-training. Our experiments show pre-training provides rich prior information and help the QR task achieve strong performance. We also show joint pre-training with NLU hypotheses has further benefit. Finally, after pre-training, we find a small set of rewrite pairs is enough to fine-tune the QR model to outperform a strong baseline by full training on all QR training data.

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