Interpreting Indirect Answers to Yes-No Questions in Multiple Languages
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Abstract (arXiv)
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).
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 31 of its 57 references that are in the map. First posted on arXiv 20 Oct 2023.
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31 of 57 references.
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