Interpreting Indirect Answers to Yes-No Questions in Multiple Languages
Zijie Wang, Md Mosharaf Hossain, Shivam Mathur, Terry Cruz Melo, Kadir Bulut Ozler, Keun Hee Park, Jacob Quintero, MohammadHossein Rezaei
Abstract
Yes-no questions expect a yes or no for an answer, but people often skip polar keywords. Instead, they answer with long explanations that must be interpreted. In this paper, we focus on this challenging problem and release new benchmarks in eight languages. We present a distant supervision approach to collect training data, and demonstrate that direct answers (i.e., with polar keywords) are useful to train models to interpret indirect answers (i.e., without polar keywords). We show that monolingual fine-tuning is beneficial if training data can be obtained via distant supervision for the language of interest (5 languages). Additionally, we show that cross-lingual fine-tuning is always beneficial (8 languages).
BibTeX
@inproceedings{
wang2023interpreting,
title={Interpreting Indirect Answers to Yes-No Questions in Multiple Languages},
author={Zijie Wang and Md Mosharaf Hossain and Shivam Mathur and Terry Cruz Melo and Kadir Bulut Ozler and Keun Hee Park and Jacob Quintero and MohammadHossein Rezaei and Shreya Nupur Shakya and Md Nayem Uddin and Eduardo Blanco},
booktitle={The 2023 Conference on Empirical Methods in Natural Language Processing},
year={2023},
url={https://openreview.net/forum?id=tEN5ONyUre}
}