Determining Question-Answer Plausibility in Crowdsourced Datasets Using Multi-Task Learning
Rachel Gardner, Maya Varma, Clare Zhu, Ranjay Krishna · 2020
Datasets extracted from social networks and online forums are often prone to the pitfalls of natural language, namely the presence of unstructured and noisy data.In this work, we seek to enable the collection of high-quality question-answer datasets from social media by proposing a novel task for automated quality analysis and data cleaning: question-answer (QA) plausibility.Given a machine or usergenerated question and a crowd-sourced response from a social media user, we determine if the question and response are valid; if so, we identify the answer within the free-form response.We design BERT-based models to perform the QA plausibility task, and we evaluate the ability of our models to generate a clean, usable question-answer dataset.Our highestperforming approach consists of a singletask model which determines the plausibility of the question, followed by a multitask model which evaluates the plausibility of the response as well as extracts answers (Question Plausibility AUROC=0.75, Response Plausibility AUROC=0.78,Answer Extraction F1=0.665).