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Shaping representations through communication: community size effect in artificial learning systems ...
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Learning and Evaluating General Linguistic Intelligence ...
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Yogatama, Dani; d'Autume, Cyprien de Masson; Connor, Jerome; Kocisky, Tomas; Chrzanowski, Mike; Kong, Lingpeng; Lazaridou, Angeliki; Ling, Wang; Yu, Lei; Dyer, Chris; Blunsom, Phil. - : arXiv, 2019
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Abstract:
We define general linguistic intelligence as the ability to reuse previously acquired knowledge about a language's lexicon, syntax, semantics, and pragmatic conventions to adapt to new tasks quickly. Using this definition, we analyze state-of-the-art natural language understanding models and conduct an extensive empirical investigation to evaluate them against these criteria through a series of experiments that assess the task-independence of the knowledge being acquired by the learning process. In addition to task performance, we propose a new evaluation metric based on an online encoding of the test data that quantifies how quickly an existing agent (model) learns a new task. Our results show that while the field has made impressive progress in terms of model architectures that generalize to many tasks, these models still require a lot of in-domain training examples (e.g., for fine tuning, training task-specific modules), and are prone to catastrophic forgetting. Moreover, we find that far from solving ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG; Machine Learning stat.ML
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URL: https://dx.doi.org/10.48550/arxiv.1901.11373 https://arxiv.org/abs/1901.11373
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Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input ...
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The RepEval 2017 Shared Task: Multi-Genre Natural Language Inference with Sentence Representations ...
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The LAMBADA dataset: Word prediction requiring a broad discourse context ...
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Combining Language and Vision with a Multimodal Skip-gram Model ...
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Improving zero-shot learning by mitigating the hubness problem ...
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Improving zero-shot learning by mitigating the hubness problem ...
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From Visual Attributes to Adjectives through Decompositional Distributional Semantics ...
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Improving zero-shot learning by mitigating the hubness problem ...
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Jointly optimizing word representations for lexical and sentential tasks with the C-PHRASE model
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“Look, some green circles!”: learning to quantify from images
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"The red one!": on learning to refer to things based on discriminative properties
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