Papers by Ameer Saadat-Yazdi

2 papers
Uncovering Implicit Inferences for Improved Relational Argument Mining (2023.eacl-main)

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Challenge: Argument mining attempts to extract arguments and their structure from unstructured texts.
Approach: They propose a generative neuro-symbolic approach to finding inference chains that connect argument pairs by using the Commonsense Transformer.
Outcome: The proposed approach outperforms the state-of-the-art by 2-5% in F1 score on three datasets.
Beyond Recognising Entailment: Formalising Natural Language Inference from an Argumentative Perspective (2024.acl-long)

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Challenge: Existing methods for recognizing textual entailment lack a standardized definition of inference, making it difficult to compare methods trained on different datasets.
Approach: They propose a rigorous approach to align entailment recognition with argumentation theory by using a tool to assist humans in annotating arguments according to the PTA.
Outcome: The proposed model is based on a human-trained dataset and provides insights into non-expert annotator training.

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