Generating Synthetic Data for Neural Keyword-to-Question Models
This addresses the challenge of semantic ambiguity in search queries for users, but it is incremental as it builds on existing neural translation techniques.
The paper tackles the problem of training neural keyword-to-question models by generating synthetic data from a small seed set, as large-scale labeled data is unavailable, and demonstrates feasibility through automatic and manual evaluations.
Search typically relies on keyword queries, but these are often semantically ambiguous. We propose to overcome this by offering users natural language questions, based on their keyword queries, to disambiguate their intent. This keyword-to-question task may be addressed using neural machine translation techniques. Neural translation models, however, require massive amounts of training data (keyword-question pairs), which is unavailable for this task. The main idea of this paper is to generate large amounts of synthetic training data from a small seed set of hand-labeled keyword-question pairs. Since natural language questions are available in large quantities, we develop models to automatically generate the corresponding keyword queries. Further, we introduce various filtering mechanisms to ensure that synthetic training data is of high quality. We demonstrate the feasibility of our approach using both automatic and manual evaluation. This is an extended version of the article published with the same title in the Proceedings of ICTIR'18.