# Generating Synthetic Data for Neural Keyword-to-Question Models

arXiv: https://arxiv.org/abs/1807.05324 · HTML: https://mlsanity.com/paper/1807.05324/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2018.

## Built on by (most cited first, 1 of 1)

- [Generate-then-Retrieve: Intent-Aware FAQ Retrieval in Product Search](https://mlsanity.com/paper/2306.03411/) (2023)

## Abstract (arXiv)

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.