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Entity-Enriched Neural Models for Clinical Question Answering
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In: arXiv (2021)
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An empirical investigation of neural methods for content scoring of science explanations
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Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational Representations ...
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Complementary Systems for Off-Topic Spoken Response Detection ...
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Multidirectional Associative Optimization of Function-Specific Word Representations ...
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Will-They-Won't-They: A Very Large Dataset for Stance Detection on Twitter ...
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Investigating the Effect of Auxiliary Objectives for the Automated Grading of Learner English Speech Transcriptions ...
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Improving Bilingual Lexicon Induction with Unsupervised Post-Processing of Monolingual Word Vector Spaces ...
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Improving Bilingual Lexicon Induction with Unsupervised Post-Processing of Monolingual Word Vector Spaces
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Investigating the Effect of Auxiliary Objectives for the Automated Grading of Learner English Speech Transcriptions
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Will-They-Won't-They: A Very Large Dataset for Stance Detection on Twitter
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Complementary Systems for Off-Topic Spoken Response Detection
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Raina, Vatsal; Gales, Mark; Knill, Katherine. - : Association for Computational Linguistics, 2020. : https://aclanthology.org/volumes/2020.bea-1/, 2020. : INNOVATIVE USE OF NLP FOR BUILDING EDUCATIONAL APPLICATIONS, 2020
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Multidirectional Associative Optimization of Function-Specific Word Representations
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Abstract:
We present a neural framework for learning associations between interrelated groups of words such as the ones found in Subject-Verb-Object (SVO) structures. Our model induces a joint function-specific word vector space, where vectors of e.g. plausible SVO compositions lie close together. The model retains information about word group membership even in the joint space, and can thereby effectively be applied to a number of tasks reasoning over the SVO structure. We show the robustness and versatility of the proposed framework by reporting state-of-the-art results on the tasks of estimating selectional preference and event similarity. The results indicate that the combinations of representations learned with our task-independent model outperform task-specific architectures from prior work, while reducing the number of parameters by up to 95%.
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URL: https://www.repository.cam.ac.uk/handle/1810/306833 https://doi.org/10.17863/CAM.53924
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Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational Representations
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