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Workshop: Human in the Loop Learning (HiLL) Workshop at NeurIPS 2022

ArgAnalysis35K - A large scale dataset for Argument Quality Detection

Omkar Joshi · Priya Pitre · Dr. Mrs. Yashodhara V. Haribhakta


Argument Quality Detection is an emerging field in NLP which has seen significant recent development. However, existing datasets in this field suffer from a lack of quality, quantity and diversity of topics and arguments, specifically the presence of vague arguments that are not persuasive in nature. In this paper, we leverage a combined experience of 10+ years of Parliamentary Debating to create a dataset that covers significantly more topics and has a wide range of sources to capture more diversity of opinion. With 35k high-quality arguments, this is also the largest dataset of its kind to our knowledge. In addition to this contribution, we introduce an innovative argument scoring system based on instance-level annotator reliability and propose a quantitative model of scoring the relevance of arguments to a range of topics.

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