In conversational search, the user's real search intent for the current turn is dependent on the previous conversation history. It is challenging to determine a good search query from the whole conversation context. To avoid the expensive re-training of the query encoder, most existing methods try to learn a rewriting model to de-contextualize the current query by mimicking the manual query rewriting. However, manually rewritten queries are not always the best search queries. Training a rewriting model on them would limit the model's ability to produce good search queries. Another useful hint is the potential answer to the question. In this paper, we propose ConvGQR, a new framework to reformulate conversational queries based on generative pre-trained language models (PLMs), one for query rewriting and another for generating potential answers. By combining both, ConvGQR can produce better search queries. In addition, to relate query reformulation to retrieval performance, we propose a knowledge infusion mechanism to optimize both query reformulation and retrieval. Extensive experiments on four conversational search datasets demonstrate the effectiveness of ConvGQR.

翻译：在对话式搜索中，用户当前轮次的真实搜索意图依赖于先前的对话历史。从整个对话上下文中确定一个良好的搜索查询具有挑战性。为避免查询编码器昂贵的重新训练，现有方法大多尝试通过学习一个改写模型，通过模仿人工查询改写来对当前查询进行去语境化。然而，人工改写的查询并不总是最佳的搜索查询。在此类改写上训练模型会限制其生成优质搜索查询的能力。另一个有用的线索是问题的潜在答案。本文提出ConvGQR，一种基于生成式预训练语言模型（PLMs）重构对话查询的新框架——一个用于查询改写，另一个用于生成潜在答案。通过结合两者，ConvGQR能够生成更优的搜索查询。此外，为将查询重构与检索性能关联起来，我们提出一种知识注入机制，以同时优化查询重构与检索过程。在四个对话式搜索数据集上的大量实验证明了ConvGQR的有效性。