"Graph Enhanced BERT for Query Understanding" by Juanhui Li, Wei Zeng et al.
 

Graph Enhanced BERT for Query Understanding

Document Type

Conference Proceeding

Publication Date

7-18-2023

Abstract

Query understanding plays a key role in exploring users' search intents. However, it is inherently challenging since it needs to capture semantic information from short and ambiguous queries and often requires massive task-specific labeled data. In recent years, pre-trained language models (PLMs) have advanced various natural language processing tasks because they can extract general semantic information from large-scale corpora. However, directly applying them to query understanding is sub-optimal because existing strategies rarely consider to boost the search performance. On the other hand, search logs contain user clicks between queries and urls that provide rich users' search behavioral information on queries beyond their content. Therefore, in this paper, we aim to fill this gap by exploring search logs. In particular, we propose a novel graph-enhanced pre-training framework, GE-BERT, which leverages both query content and the query graph to capture both semantic information and users' search behavioral information of queries. Extensive experiments on offline and online tasks have demonstrated the effectiveness of the proposed framework.

Identifier

85168698326 (Scopus)

ISBN

[9781450394086]

Publication Title

SIGIR 2023 Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

External Full Text Location

https://doi.org/10.1145/3539618.3591845

First Page

3315

Last Page

3319

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