Charles Jochim

TL;DR: This model is a first step towards automatic leaderboard construction, e.g., in the NLP domain, aimed at automatically extracting task, dataset, metric and score from NLP papers, towards the automatic construction of leaderboards.

TL;DR: In this paper, the authors present Project Debater, an autonomous debating system that can engage in a competitive debate with humans, and provide a complete description of the system's architecture, a thorough and systematic evaluation of its operation across a wide range of debate topics.

TL;DR: This work uses a standard classification scheme for citations that was developed independently of automatic classification and therefore is not bound to any particular citation application, and introduces new features designed for citation classification and compares them experimentally with previously proposed citation features, showing that these new features improve classification accuracy.

TL;DR: This work shows that both accuracy and coverage can be significantly improved through automatic expansion of the initial lexicon, and developed a set of contextual features that further improves the state-of-the-art for this task.

TL;DR: This paper studies the end-to-end construction of an argumentation knowledge graph that is intended to support argument synthesis, argumentative question answering, or fake news detection, among others.