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BiSECT: Learning to Split and Rephrase Sentences with Bitexts ...
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Abstract:
An important task in NLP applications such as sentence simplification is the ability to take a long, complex sentence and split it into shorter sentences, rephrasing as necessary. We introduce a novel dataset and a new model for this `split and rephrase' task. Our BiSECT training data consists of 1 million long English sentences paired with shorter, meaning-equivalent English sentences. We obtain these by extracting 1-2 sentence alignments in bilingual parallel corpora and then using machine translation to convert both sides of the corpus into the same language. BiSECT contains higher quality training examples than previous Split and Rephrase corpora, with sentence splits that require more significant modifications. We categorize examples in our corpus, and use these categories in a novel model that allows us to target specific regions of the input sentence to be split and edited. Moreover, we show that models trained on BiSECT can perform a wider variety of split operations and improve upon previous ... : 9 pages, 9 figures. Long paper to appear in Empirical Methods in Natural Language Processing 2021 (EMNLP 2021) ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/2109.05006
https://dx.doi.org/10.48550/arxiv.2109.05006
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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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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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