Generating Synthetic Data for Neural Keyword-to-Question Models

TL;DR

This paper presents a method to generate synthetic keyword-question pairs from a small seed set, enabling neural models to better disambiguate search queries by translating questions into keywords.

Contribution

The authors introduce a novel approach to generate large-scale synthetic training data for keyword-to-question models using neural translation and filtering techniques.

Findings

Synthetic data improves model performance

Filtering mechanisms ensure high-quality training data

Feasibility demonstrated through evaluations

Abstract

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.