Graph Enhanced Query Rewriting For Spoken Language Understanding System

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, 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, which demonstrates the effectiveness of the proposed method.