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 Shreya Nupur Shakya Md Nayem Uddin Eduardo Blanco
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. We also demonstrate that direct answers (i.e., with
polar keywords) are useful to train models to interpret indirect answers (i.e.,
without polar keywords). Experimental results demonstrate 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).