Graph Enhanced Query Rewriting for Spoken Language Understanding System
Siyang Yuan, Saurabh Gupta, Xing Fan, Derek Liu, Yang Liu, Chenlei Guo
Abstract
Query rewriting (QR) is an increasingly important component in voice assistant systems to reduce customer friction caused by errors in a spoken language understanding pipeline. These errors originate from various sources such as Automatic Speech Recognition (ASR) and Natural Language Understanding (NLU) modules. In this work, we construct a user interaction graph from their queries using data mined from a Markov Chain Model [1], and introduce a self-supervised pre-training process for learning query embeddings by leveraging the recent developments in Graph Representation Learning (GRL). We then fine-tune these embeddings with weak supervised data for the query rewriting task, and observe improvement over the neural retrieval baseline system, demonstrating the effectiveness of the proposed method.
BibTeX
@inproceedings{icassp2021_graphenhancedque,
title = {Graph Enhanced Query Rewriting for Spoken Language Understanding System},
author = {Siyang Yuan and Saurabh Gupta and Xing Fan and Derek Liu and Yang Liu and Chenlei Guo},
booktitle = {ICASSP 2021},
year = {2021}
}