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Ranking Facts for Explaining Answers to Elementary Science Questions ...
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SemEval-2021 Task 11: NLPContributionGraph - Structuring Scholarly NLP Contributions for a Research Knowledge Graph ...
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Pattern-based Acquisition of Scientific Entities from Scholarly Article Titles ...
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Fine-tuning BERT with Focus Words for Explanation Regeneration ...
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Abstract:
Explanation generation introduced as the world tree corpus (Jansen et .al., 2018) is an emerging NLP task involving multi-hop inference for explaining the correct answer in multiple-choice QA. It is a challenging task evidenced by low state-of-the-art performances(below 60% in F-score) demonstrated on the task. Of the state-of-the-art approaches, fine-tuned transformer-based (Vaswani et al., 2017)BERT models have shown great promise toward continued system performance improvements compared with approaches relying on surface-level cues alone that demonstrate performance saturation. In this work, we take a novel direction by addressing a particular linguistic characteristic of the data—we intro-duce a novel and lightweight focus feature in the transformer-based model and examine task improvements. Our evaluations reveal a significantly positive impact of this lightweight focus feature achieving the highest scores, second only to a significantly computationally intensive system. ...
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Keyword:
Computer and Information Science; Natural Language Processing; Neural Network
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URL: https://underline.io/lecture/6427-fine-tuning-bert-with-focus-words-for-explanation-regeneration https://dx.doi.org/10.48448/htqw-dp71
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The STEM-ECR Dataset: Grounding Scientific Entity References in STEM Scholarly Content to Authoritative Encyclopedic and Lexicographic Sources ...
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The STEM-ECR Dataset: Grounding Scientific Entity References in STEM Scholarly Content to Authoritative Encyclopedic and Lexicographic Sources ...
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Domain-Independent Extraction of Scientific Concepts from Research Articles ...
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Domain-independent Extraction of Scientific Concepts from Research Articles ...
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