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BiSECT: Learning to Split and Rephrase Sentences with Bitexts ...
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BiSECT: Learning to Split and Rephrase Sentences with Bitexts ...
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Natural language processing methods are sensitive to sub-clinical linguistic differences in schizophrenia spectrum disorders
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In: NPJ Schizophr (2021)
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Towards a Practically Useful Text Simplification System
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In: Dissertations available from ProQuest (2021)
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Complexity-Weighted Loss and Diverse Reranking for Sentence Simplification ...
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Comparison of Diverse Decoding Methods from Conditional Language Models ...
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Abstract:
While conditional language models have greatly improved in their ability to output high-quality natural language, many NLP applications benefit from being able to generate a diverse set of candidate sequences. Diverse decoding strategies aim to, within a given-sized candidate list, cover as much of the space of high-quality outputs as possible, leading to improvements for tasks that re-rank and combine candidate outputs. Standard decoding methods, such as beam search, optimize for generating high likelihood sequences rather than diverse ones, though recent work has focused on increasing diversity in these methods. In this work, we perform an extensive survey of decoding-time strategies for generating diverse outputs from conditional language models. We also show how diversity can be improved without sacrificing quality by over-sampling additional candidates, then filtering to the desired number. ... : 11 pages, Association of Computational Linguistics (ACL 2019) ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.1906.06362 https://arxiv.org/abs/1906.06362
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Learning translations via images with a massively multilingual image dataset
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Simplification Using Paraphrases and Context-Based Lexical Substitution
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In: Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies ; https://hal.archives-ouvertes.fr/hal-01838519 ; Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics, Jun 2018, Nouvelle Orléans, United States (2018)
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