2018
EMNLP
EMNLP 2018
Limitations in learning an interpreted language with recurrent models
Abstract
AbstractIn this submission I report work in progress on learning simplified interpreted languages by means of recurrent models. The data is constructed to reflect core properties of natural language as modeled in formal syntax and semantics. Preliminary results suggest that LSTM networks do generalise to compositional interpretation, albeit only in the most favorable learning setting.
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Interdisciplinary Bridge
— Deep Learning and Interdisciplinary and Machine Learning and Natural Language Processing
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Keyword Pioneer
— compositional interpretation
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio