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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 8 of 8 1 Ranking Facts for Explaining Answers to Elementary Science Questions ... D'Souza, Jennifer; Mulang', Isaiah Onando; Auer, Soeren. - : arXiv, 2021 BASE Show details 2 SemEval-2021 Task 11: NLPContributionGraph - Structuring Scholarly NLP Contributions for a Research Knowledge Graph ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Auer, Sören; D'Souza, Jennifer. - : Underline Science Inc., 2021 BASE Show details 3 Pattern-based Acquisition of Scientific Entities from Scholarly Article Titles ... D'Souza, Jennifer; Auer, Soeren. - : arXiv, 2021 BASE Show details 4 Fine-tuning BERT with Focus Words for Explanation Regeneration ... The 28th International Conference on Computational Linguistics 2020; Auer, Sören; D'Souza, Jennifer; Mulang', Isaiah Onando. - : Underline Science Inc., 2020 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. ... Keyword: Computer and Information Science; Natural Language Processing; Neural Network URL: https://underline.io/lecture/6427-fine-tuning-bert-with-focus-words-for-explanation-regeneration https://dx.doi.org/10.48448/htqw-dp71 BASE Hide details 5 The STEM-ECR Dataset: Grounding Scientific Entity References in STEM Scholarly Content to Authoritative Encyclopedic and Lexicographic Sources ... D'Souza, Jennifer; Hoppe, Anett; Brack, Arthur. - : arXiv, 2020 BASE Show details 6 The STEM-ECR Dataset: Grounding Scientific Entity References in STEM Scholarly Content to Authoritative Encyclopedic and Lexicographic Sources ... D'Souza, Jennifer; Hoppe, Anett; Brack, Arthur. - : Paris : European Language Resources Association, 2020 BASE Show details 7 Domain-Independent Extraction of Scientific Concepts from Research Articles ... Brack, Arthur; D'Souza, Jennifer; Hoppe, Anett. - : Cham : Springer, 2020 BASE Show details 8 Domain-independent Extraction of Scientific Concepts from Research Articles ... Brack, Arthur; D'Souza, Jennifer; Hoppe, Anett. - : arXiv, 2020 BASE Show details

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