Graph Enhanced BERT for Query Understanding
Graph Enhanced BERT for Query Understanding
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DOI：
10.1145/3539618.3591845
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发表时间：
2022-04
期刊：
影响因子：
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通讯作者：
Juanhui Li;Yao Ma;Weizhen Zeng;Suqi Cheng;Jiliang Tang;Shuaiqiang Wang;Dawei Yin
中科院分区：
文献类型：
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作者：
Juanhui Li;Yao Ma;Weizhen Zeng;Suqi Cheng;Jiliang Tang;Shuaiqiang Wang;Dawei Yin
查询理解在探索用户的搜索意图和方便用户定位他们最需要的信息方面起着关键作用。然而，它本身就具有挑战性，因为它需要从简短和模糊的查询中捕获语义信息，并且通常需要大量特定于任务的标记数据。近年来，预训练语言模型（PLMs）由于能够从大规模语料库中提取通用语义信息而推动了各种自然语言处理任务的发展。然而，直接将它们应用于查询理解是次优的，因为现有的策略很少考虑提高搜索性能。另一方面，搜索日志包含用户在查询和url之间的点击，这些url提供了丰富的用户对超出其内容的查询的搜索行为信息。因此，在本文中，我们的目标是通过探索搜索日志来填补这一空白。特别地，我们提出了一种新的图增强预训练框架，GE-BERT，它同时利用了查询内容和查询图。该模型在一个查询图上进行训练，其中节点是查询，如果两个查询导致相同的url点击，则将两个查询连接起来，以捕获查询的语义信息和用户搜索行为信息。大量的离线和在线任务实验证明了该框架的有效性。
Query understanding plays a key role in exploring users' search intents and facilitating users to locate their most desired information. 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. The model is trained on a query graph where nodes are queries and two queries are connected if they lead to clicks on the same urls, 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.