2018
EMNLP
EMNLP 2018
An Encoder-Decoder Approach to the Paradigm Cell Filling Problem
Abstract
AbstractThe Paradigm Cell Filling Problem in morphology asks to complete word inflection tables from partial ones. We implement novel neural models for this task, evaluating them on 18 data sets in 8 languages, showing performance that is comparable with previous work with far less training data. We also publish a new dataset for this task and code implementing the system described in this paper.
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Interdisciplinary Bridge
— Deep Learning and Machine Learning
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Keyword Pioneer
— paradigm cell filling
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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