论文标题
深入加强查询重新重新进行信息检索
Deep Reinforced Query Reformulation for Information Retrieval
论文作者
论文摘要
长期以来，查询重新制定一直是减轻信息检索中词汇不匹配问题的关键机制，例如，通过使用相关查询术语或生成查询的释义来扩展查询。在这项工作中，我们提出了一个深厚的查询重新印象（DRQR）模型，以自动生成对查询的新重新进行。为了鼓励模型生成可以在执行检索任务时可以实现高性能的查询，我们将查询性能预测纳入我们的奖励功能。此外，为了在信息检索的背景下评估重新计算查询的质量，我们首先训练DRQR模型，然后将检索排名模型应用于获得的重新重新查询。实验是在TREC 2020深度学习跟踪MSMARCO文档排名数据集上进行的。我们的结果表明，我们提出的模型在执行检索任务时优于几个查询重新制定模型基线。此外，与各种检索模型（例如查询扩展和BERT）结合时，还可以观察到改进。
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.
