Keun Park


2023

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Interpreting Indirect Answers to Yes-No Questions in Multiple Languages
Zijie Wang | Md Hossain | Shivam Mathur | Terry Melo | Kadir Ozler | Keun Park | Jacob Quintero | MohammadHossein Rezaei | Shreya Shakya | Md Uddin | Eduardo Blanco
Findings of the Association for Computational Linguistics: EMNLP 2023

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).

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Interpreting Answers to Yes-No Questions in User-Generated Content
Shivam Mathur | Keun Park | Dhivya Chinnappa | Saketh Kotamraju | Eduardo Blanco
Findings of the Association for Computational Linguistics: EMNLP 2023

Interpreting answers to yes-no questions in social media is difficult. Yes and no keywords are uncommon, and the few answers that include them are rarely to be interpreted what the keywords suggest. In this paper, we present a new corpus of 4,442 yes-no question-answer pairs from Twitter. We discuss linguistic characteristics of answers whose interpretation is yes or no, as well as answers whose interpretation is unknown. We show that large language models are far from solving this problem, even after fine-tuning and blending other corpora for the same problem but outside social media.