Search-Oriented Conversational Query Editing

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 editingbased CQR model tailored for conversational search. In EDIRCS, most of the rewrite tokens are selected from the dialogue in a nonautoregressive 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-theart CQR models on three conversational search benchmarks while having low rewriting latency, and is more robust to out-of-domain search dialogues and long dialogue context.