C onv GQR : Generative Query Reformulation for Conversational Search (2023)

Abstract: In conversational search, the user’s real search intent for the current conversation 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. Thus, training a rewriting model on them would lead to sub-optimal queries. Another useful information to enhance the search query 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 the retrieval task, 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.

## introduction conversational search @xcite is a rapidly developing branch of information retrieval, which aims to satisfy complex information needs through multi-turn conversations. the main challenge is to determine users' real search intents based on the interaction context and formulate good search queries accordingly. existing methods can be roughly categorized into two groups. the first group directly uses the whole context as a query and trains a model to determine the relevance between the long context and passages (qu et al., 2020; hashemi et al., 2020; yu et al., 2021; lin et al., 2021b; mao * corresponding authors. @xcite @xcite . this approach requires additional training of retriever to take the long context as input, which is not always feasible @xcite . what is available in practice is a general retriever (e.g., adhoc search retriever) that uses a stand-alone query. the second group of approaches aims at producing a de-contextualized query using query reformulation techniques @xcite . such a query can be submitted to any off-the-shelf retrievers. we focus on this second approach. two types of query reformulation techniques have been widely studied in the literature, i.e., query rewriting and query expansion. the former trains a generative model to rewrite the current query to mimic the human-rewritten one @xcite , while the latter focuses on expanding the current query by relevant terms selected from the context @xcite . although both approaches achieve promising results, they are all studied separately. two important limitations are observed: (1) query rewriting and query expansion can produce different effects. query rewriting tends to deal with ambiguous queries and add missing tokens, while query expansion aims to add supplementary information to the query. both effects are important for query reformulation. it is thus beneficial to use both of them. (2) previous query rewriting models have been optimized to produce human-rewritten queries, independently from the passage ranking task. even though humanrewritten queries usually perform better than the original queries, existing studies have shown that they may not be the best search queries alone @xcite . therefore, it is useful to incorporate additional criteria directly related to ranking performance when reformulating a query. as shown in fig. 1 (left), although the human-rewritten query recovers the crucial missing information (i.e. "goat") from the context, it is ## related work conversational query reformulation the intuitive idea is that a well-formulated search query from the conversation context can be submitted to an off-the-shelf retriever for search without modifying it. query rewriting and query expansion are two typical query reformulation methods. query rewriting aims to train a rewriting model to mimic human-rewritten queries. this approach is shown to be able to solve the ambiguous problem and recover some missing elements (e.g. anaphora) from the context @xcite @xcite . however, @xcite al. (2021) and lin et al. (2021b) argue that the human-rewritten queries might not necessarily be the optimal queries. @xcite enhances the rewriting model by leveraging reinforcement learning. however, it turns out that reinforcement learning requires a long time for training. to be more efficient, @xcite proposes a query expansion method by selecting the terms via the normalization score of their embeddings but still needs to re-train a retriever. some earlier query expansion methods @xcite ) also focus on selecting useful terms from conversational context. the previous studies show that query rewriting and session query rep. @xmath0. dense encoder cls … … passages … pos. passages rep. session query rep. mse loss knowledge infusion session query rep. @xmath1. passages rep. session query rep. mse loss knowledge infusion generative plm -rewrite dense retriever (frozen) generative plm -expansion match function dense encoder reformulated query rep. sparse retriever rank list (dense) rank list (sparse) figure 2: overview of convgqr. two generative plms are used to generate a rewritten query and expansion terms for both training and inference. the knowledge infusion mechanism (dashed boxes) is only applied during training. ## experiments datasets following previous studies @xcite , four conversational search datasets are used for our experiments. the topiocqa (adlakha et al., 2022) and qrecc @xcite datasets are used for normal query reformulation training. two other widely used trec cast datasets @xcite are only used for zero-shot evaluation as no training data is provided. the statistics and more details are provided in appendix a. evaluation metrics to evaluate the retrieval results, we use four standard evaluation metrics: mrr, ndcg@3, recall@10 and recall@100, as previous studies @xcite @xcite . we adopt the pytrec_eval tool (van gysel and de rijke, 2018) for metric computation. baselines we mainly compare convgqr with the following query reformulation (qr) baselines for both dense and sparse retrieval: (1) raw: the query of current turn without reformulation. for zero-shot scenario, in addition to the quretec method originally fine-tuned on quac datasets @xcite , we also perform comparisons with (8) transformer++ @xcite : a gpt-2 based qr model fine-tuned on canard dataset @xcite . (9) query rewriter @xcite : a gpt-2 based qr model fine-tuned on large-scale search session data. besides, the results of human-rewritten queries in the original datasets are also provided. ## conclusion in this paper, we present a new conversational query reformulation framework, convgqr, which integrates query rewriting and query expansion toward generating more effective search queries through a new knowledge infusion mechanism. extensive experimental results on four public datasets demonstrate the superior effectiveness of our model for conversational search. we also carried out detailed analyses to understand the effects of each component of convgqr on the performance improvements.