Papers with question-answering models

3 papers
A Dataset of Argumentative Dialogues on Scientific Papers (2023.acl-long)

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Challenge: Recent advances in question-answering models have made them a great asset in accessing the content of scientific papers.
Approach: They propose to use a dataset of 41 argumentative dialogues between scientists on 20 NLP papers to improve and evaluate their question-answering models.
Outcome: The proposed dataset includes both exploratory and argumentative questions and answers in a dialogue discourse on a scientific paper.
Social IQa: Commonsense Reasoning about Social Interactions (D19-1)

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Challenge: Social IQa contains 38,000 multiple choice questions for probing emotional and social intelligence in a variety of everyday situations.
Approach: They propose a crowdsourcing framework that collects commonsense questions along with correct and incorrect answers about social interactions.
Outcome: The proposed framework mitigates stylistic artifacts in incorrect answers by asking workers to provide the right answer to a different but related question.
Prompt-based Zero-shot Relation Extraction with Semantic Knowledge Augmentation (2024.lrec-main)

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Challenge: Existing approaches to recognize unseen relations for which there are no training instances are lacking in the real-world setting.
Approach: They propose a prompt-based model with semantic knowledge augmentation to recognize unseen relations under zero-shot setting.
Outcome: The proposed model outperforms existing methods under zero-shot setting on three datasets.

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