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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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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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Abstract:
Building intelligent agents that can communicate with and learn from humans in natural language is of great value. Supervised language learning is limited by the ability of capturing mainly the statistics of training data, and is hardly adaptive to new scenarios or flexible for acquiring new knowledge without inefficient retraining or catastrophic forgetting. We highlight the perspective that conversational interaction serves as a natural interface both for language learning and for novel knowledge acquisition and propose a joint imitation and reinforcement approach for grounded language learning through an interactive conversational game. The agent trained with this approach is able to actively acquire information by asking questions about novel objects and use the just-learned knowledge in subsequent conversations in a one-shot fashion. Results compared with other methods verified the effectiveness of the proposed approach. ... : ACL 2018 ...
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Keyword:
Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.1805.00462 https://arxiv.org/abs/1805.00462
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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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