Search-Oriented Conversational Query Editing (2023)

Abstract: Conversational query rewriting (CQR) realizes conversational search by reformulating the search dialogue into a standalone rewrite. However, existing CQR models either are not learned toward improving the downstream search performance or inefficiently generate the rewrite token-by-token from scratch while neglecting the fact that the search dialogue often has a large overlap with the rewrite. In this paper, we propose EdiRCS, a new text editing-based CQR model tailored for conversational search. In EdiRCS, most of the rewrite tokens are selected from the dialogue in a non-autoregressive fashion and only a few new tokens are generated to supplement the final rewrite, which makes EdiRCS highly efficient. In particular, the learning of EdiRCS is augmented with two search-oriented objectives, including contrastive ranking augmentation and contextualization knowledge transfer, which effectively improve it to select and generate more useful tokens from the view of retrieval. We show that EdiRCS outperforms state-of-the-art CQR models on three conversational search benchmarks while having low rewriting latency, and is robust to out-of-domain search dialogues and long dialogue contexts.

## introduction with the rise of intelligent assistants (e.g., siri and alexa), conversational search is becoming a new search paradigm of the future @xcite . as users can interact with the search engine in the form of natural dialogue, one of the main challenges of conversational search is to accurately understand users' search intents within the dialogue context @xcite . inspired by the success of dense retrieval in adhoc search @xcite , recent studies @xcite @xcite show that using a similar contrastive learning approach to train a conversational dense retriever with a dual encoder architecture can effectively resolve such a complex context understanding problem. however, since the ad-hoc search systems have been built, deployed, and optimized for a long time in the industry, replacing the wellestablished ad-hoc retriever with a totally newtrained conversational dense retriever would be too expensive and even not realistic in the current early days of conversational search. as such, another type of method, i.e., conversational query rewriting @xcite , which explicitly reformulates the whole search dialogue into a contextindependent query rewrite and thus can be seamlessly incorporated into any existing ad-hoc search pipelines to realize conversational search, shows greater practical value. currently, a typical cqr model is built by fine-tuning an autoregressive pretrained language model (plm) using search dialogue (input) and manual rewrite (target) text pairs. despite the promising results obtained, we argue that it has the following two significant limitations, as shown in figure 1 . first, the single training objective to simply fit the manual rewrite is not aligned with our ultimate goal, i.e., achieving better performance on the downstream conversational search task. on one hand, the quality of manual rewrites is probably not the best from the view of retrieval, since humans are only instructed to rewrite queries to be selfcontained outside the dialogue context, but have no knowledge of the downstream retrieval process. on the other hand, no ranking signals from the retrieval side are taken into account when training the model @xcite . second, for conversational query rewriting, many expected rewrite tokens can be found in the search dialogue in most cases. however, the autoregressive rewriting model still generates the rewrite completely from scratch, ! " : how many rings does he have? " ! " : how many nba championship rings does michael jordan have? ## our model: edircs we present edircs, a cqr model that aims to achieve more effective and efficient conversational search based on text editing and augmentation from search-oriented objectives. on the whole, as shown in figure 2 , edircs follows a selecting-thengenerating text editing architecture, which first selects a few necessary tokens from the session and then generates more useful new tokens to supplement the rewrite. in particular, both the session token selection and new token generation processes benefit from the proposed search-oriented learning to help the generated rewrite achieve better conversational search performance. we finally summarize the whole training and inference processes. ## conclusion in this paper, we present a cqr model edircs based on the text editing paradigm for efficient and effective conversational search. edircs is augmented with two novel search-oriented objectives that can leverage the downstream retrieval information to improve the learning of query rewriting toward conversational search. experiments on three conversational search datasets demonstrate the superior effectiveness and efficiency of edircs over existing cqr models. we also show that edircs has decent robustness to out-of-domain search dialogues and difficult long context. future directions include exploring more search-oriented objectives and simultaneously improving the coherence and retrieval performance of the rewrites.