The ability to generate clarification questions i.e., questions that identify useful missing information in a given context, is important in reducing ambiguity. Humans use previous experience with similar contexts to form a global view and compare it to the given context to ascertain what is missing and what is useful in the context. Inspired by this, we propose a model for clarification question generation where we first identify what is missing by taking a difference between the global and the local view and then train a model to identify what is useful and generate a question about it. Our model outperforms several baselines as judged by both automatic metrics and humans.
Cite
@article{arxiv.2104.06828,
title = {Ask what's missing and what's useful: Improving Clarification Question Generation using Global Knowledge},
author = {Bodhisattwa Prasad Majumder and Sudha Rao and Michel Galley and Julian McAuley},
journal= {arXiv preprint arXiv:2104.06828},
year = {2021}
}
Comments
Accepted in NAACL 2021, Code is available at https://github.com/microsoft/clarification-qgen-globalinfo