# End-to-End Argument Mining for Discussion Threads Based on Parallel Constrained Pointer Architecture Source: [End-to-End Argument Mining for Discussion Threads Based on Parallel Constrained Pointer Architecture]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:11:58.696Z Authors: Gaku Morio, Katsuhide Fujita Published: 2018-09-03T11:48:28.000Z ## Abstract Argument Mining (AM) is a relatively recent discipline, which concentrates on extracting claims or premises from discourses, and inferring their structures. However, many existing works do not consider micro-level AM studies on discussion threads sufficiently. In this paper, we tackle AM for discussion threads. Our main contributions are follows: (1) A novel combination scheme focusing on micro-level inner- and inter- post schemes for a discussion thread. (2) Annotation of large-scale civic discussion threads with the scheme. (3) Parallel constrained pointer architecture (PCPA), a novel end-to-end technique to discriminate sentence types, inner-post relations, and inter-post interactions simultaneously. The experimental results demonstrate that our proposed model shows better accuracy in terms of relations extraction, in comparison to existing state-of-the-art models. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Argumentative Component Classification on CMV (test)](): Argumentative Component Classification; CMV (test); F1 (Claim): 54.2. - [Intra-turn Relation Prediction on CMV (test)](): Intra-turn Relation Prediction; CMV (test); Precision: 10. - [Inter-Turn Relation Prediction on CMV QR data](): Inter-Turn Relation Prediction; CMV QR data; Precision: 7.6. ## Original sources and implementation [Original paper]() [PDF]()