2018
ACL
ACL 2018
Neural Adversarial Training for Semi-supervised Japanese Predicate-argument Structure Analysis
Abstract
AbstractJapanese predicate-argument structure (PAS) analysis involves zero anaphora resolution, which is notoriously difficult. To improve the performance of Japanese PAS analysis, it is straightforward to increase the size of corpora annotated with PAS. However, since it is prohibitively expensive, it is promising to take advantage of a large amount of raw corpora. In this paper, we propose a novel Japanese PAS analysis model based on semi-supervised adversarial training with a raw corpus. In our experiments, our model outperforms existing state-of-the-art models for Japanese PAS analysis.
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Interdisciplinary Bridge
— Deep Learning and Machine Learning and Natural Language Processing
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Keyword Pioneer
— zero anaphora resolution
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Hot Topic Early Bird
— japanese language
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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
Authors
Topics
Machine Learning > Learning Types > Adversarial Learning
Machine Learning > Learning Types > Semi-Supervised Learning
Natural Language Processing > Understanding > Semantic Analysis
Deep Learning > Learning Types > Adversarial Learning
Natural Language Processing > Applications > Semantic Parsing
Deep Learning > Learning Types > Semi-Supervised Learning
Natural Language Processing > Applications > Semantic Analysis