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The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
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In: Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021) ; https://hal.archives-ouvertes.fr/hal-03466171 ; Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021), Aug 2021, Online, France. pp.96-120, ⟨10.18653/v1/2021.gem-1.10⟩ (2021)
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BiSECT: Learning to Split and Rephrase Sentences with Bitexts ...
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Pre-train or Annotate? Domain Adaptation with a Constrained Budget ...
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BiSECT: Learning to Split and Rephrase Sentences with Bitexts ...
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Sample data for "Design and Collection Challenges of Building an Academic Email Corpus for Linguistics and Computational Research" ...
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Sample data for "Design and Collection Challenges of Building an Academic Email Corpus for Linguistics and Computational Research" ...
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Controllable Text Simplification with Explicit Paraphrasing ...
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Abstract:
Read the paper on the folowing link: https://www.aclweb.org/anthology/2021.naacl-main.277/ Abstract: Text Simplification improves the readability of sentences through several rewriting transformations, such as lexical paraphrasing, deletion, and splitting. Current simplification systems are predominantly sequence-to-sequence models that are trained end-to-end to perform all these operations simultaneously. However, such systems limit themselves to mostly deleting words and cannot easily adapt to the requirements of different target audiences. In this paper, we propose a novel hybrid approach that leverages linguistically motivated rules for splitting and deletion, and couples them with a neural paraphrasing model to produce varied rewriting styles. We introduce a new data augmentation method to improve the paraphrasing capability of our model. Through automatic and manual evaluations, we show that our proposed model establishes a new state-of-the-art for the task, paraphrasing more often than the existing ...
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Keyword:
Artificial Intelligence; Computer Science and Engineering; Intelligent System; Natural Language Processing
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URL: https://dx.doi.org/10.48448/ze3t-sa51 https://underline.io/lecture/19581-controllable-text-simplification-with-explicit-paraphrasing
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The effectiveness of the problem-based learning in medical cell biology education: A systematic meta-analysis
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In: Medicine (Baltimore) (2021)
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Controllable text simplification with explicit paraphrasing
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An Empirical Study of Pre-trained Transformers for Arabic Information Extraction ...
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Controllable Text Simplification with Explicit Paraphrasing ...
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Interactive Grounded Language Acquisition and Generalization in a 2D World ...
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Interactive Language Acquisition with One-shot Visual Concept Learning through a Conversational Game ...
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Guided Feature Transformation (GFT): A Neural Language Grounding Module for Embodied Agents ...
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A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplification ...
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A Deep Compositional Framework for Human-like Language Acquisition in Virtual Environment ...
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A Continuously Growing Dataset of Sentential Paraphrases ...
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Spectral Entropy Can Predict Changes of Working Memory Performance Reduced by Short-Time Training in the Delayed-Match-to-Sample Task
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