Deep Reinforced Query Reformulation for Information Retrieval

By Xiao Wang, Craig Macdonald, Iadh Ounis. 2020

28 citations recorded by the source corpus.

Query reformulations have long been a key mechanism to alleviate the vocabulary-mismatch problem in information retrieval, for example by expanding the queries with related query terms or by generating paraphrases of the queries. In this work, we propose a deep reinforced query reformulation (DRQR) model to automatically generate new reformulations of the query. To encourage the model to generate queries which can achieve high performance when performing the retrieval task, we incorporate query performance prediction into our reward function. In addition, to evaluate the quality of the reformulated query in the context of information retrieval, we first train our DRQR model, then apply the retrieval ranking model on the obtained reformulated query. Experiments are conducted on the TREC 2020 Deep Learning track MSMARCO document ranking dataset. Our results show that our proposed model outperforms several query reformulation model baselines when performing retrieval task. In addition, improvements are also observed when combining with various retrieval models, such as query expansion and BERT.

Subfields: Artificial Intelligence, Information Systems, Information Retrieval & Search, Reinforcement Learning, Signal Processing

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Citations in the corpus

WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents (2022, background): “These include: 1) generation of good search queries [22, 59] and reformulation [37, 51], 2) strategic exploration for navigating through the website [55, 56, 29], 3) robust language understanding for textual state and action spaces [3, 7, 17, 44], and 4) long-term”
Generation-Augmented Retrieval for Open-Domain Question Answering (2020, methodology): “Also, some rely on timeconsuming training process like reinforcement learning (RL) (Nogueira and Cho, 2017; Liu et al., 2019; Wang et al., 2020) that is not efficient enough for OpenQA (more discussions in Sec.”
Deep Literature Survey Automation with an Iterative Workflow (2025, unknown): “First, retrieval can be imprecise and static due to reliance on a short topic description (often just a few tokens) as the retrieval query (Sun et al., 2019; Azad & Deepak, 2019; Wang et al., 2020).”