Interpreting Answers to Yes-No Questions in User-Generated Content
Shivam Mathur Keun Hee Park Dhivya Chinnappa Saketh Kotamraju Eduardo Blanco
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.