Search Results for author: Elena Musi

Found 8 papers, 1 papers with code

Analyzing the Semantic Types of Claims and Premises in an Online Persuasive Forum

no code implementations WS 2017 Christopher Hidey, Elena Musi, Alyssa Hwang, Smar Muresan, a, Kathy Mckeown

Argumentative text has been analyzed both theoretically and computationally in terms of argumentative structure that consists of argument components (e. g., claims, premises) and their argumentative relations (e. g., support, attack).

Argument Mining

ChangeMyView Through Concessions: Do Concessions Increase Persuasion?

no code implementations8 Jun 2018 Elena Musi, Debanjan Ghosh, Smaranda Muresan

Drawing from a theoretically-informed typology of concessions, we conduct an annotation task to label a set of polysemous lexical markers as introducing an argumentative concession or not and we observe their distribution in threads that achieved and did not achieve persuasion.

Rubric Reliability and Annotation of Content and Argument in Source-Based Argument Essays

1 code implementation WS 2019 Yanjun Gao, Alex Driban, Brennan Xavier McManus, Elena Musi, Patricia Davies, Smar Muresan, a, Rebecca J. Passonneau

We present a unique dataset of student source-based argument essays to facilitate research on the relations between content, argumentation skills, and assessment.

Interpreting Verbal Irony: Linguistic Strategies and the Connection to the Type of Semantic Incongruity

no code implementations3 Nov 2019 Debanjan Ghosh, Elena Musi, Kartikeya Upasani, Smaranda Muresan

Human communication often involves the use of verbal irony or sarcasm, where the speakers usually mean the opposite of what they say.

Multitask Instruction-based Prompting for Fallacy Recognition

no code implementations24 Jan 2023 Tariq Alhindi, Tuhin Chakrabarty, Elena Musi, Smaranda Muresan

To move towards solving the fallacy recognition task, we approach these differences across datasets as multiple tasks and show how instruction-based prompting in a multitask setup based on the T5 model improves the results against approaches built for a specific dataset such as T5, BERT or GPT-3.

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