Question-formed Query Suggestion

Abstract

Traditional Query Suggestion (TQS) aims to retrieve or generate completed queries given input keywords and query logs, which plays a vital role in information retrieval. Nearly all existing TQS methods obtain suggested queries, which are usually in the form of keywords or phrases. However, queries like keywords or phrases suffer from incomplete or ambiguous se-mantics. Ideally, question-formed queries are more intuitive and closer to the information needs of users, which can improve their satisfaction during a search. Motivated by this idea, thus, this paper defines a novel question-formed query suggestion task that generates question-formed queries given input keywords and web page texts. Moreover, we also propose a novel pipeline method for this novel task. Specifically, a query generation module is first employed to generate related question-formed queries given keywords and web page texts. Then, a selection module selects the most representative tops among all generated queries as the final suggestion. Extensive experiments demonstrate that our method outperforms the state-of-the-art baselines in human evaluation.