Work

Interpreting Answers to Yes-No Questions in User-Generated Content

Year: 2023

Type: conference-paper

Abstract: 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.

Source: Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP)

DOI: 10.18653/v1/2023.findings-emnlp.942

Authors: Shivam Mathur, Keun Park, Dhivya Chinnappa, Saketh Kotamraju, Eduardo Blanco

Institutions: Walmart (United States), Arizona State University, JPMorgan Chase & Co (United States), The University of Texas at Austin, University of Arizona

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: National Science Foundation

Award: CAREER: Understanding Negation in Positive Terms