2019
EMNLP
EMNLP 2019
Domain-agnostic Question-Answering with Adversarial Training
Abstract
AbstractAdapting models to new domain without finetuning is a challenging problem in deep learning. In this paper, we utilize an adversarial training framework for domain generalization in Question Answering (QA) task. Our model consists of a conventional QA model and a discriminator. The training is performed in the adversarial manner, where the two models constantly compete, so that QA model can learn domain-invariant features. We apply this approach in MRQA Shared Task 2019 and show better performance compared to the baseline model.
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Topic Pioneer
— Domain Generalization
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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Machine Learning and Natural Language Processing
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Trend Setter
— Domain Generalization
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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 > Application Areas > Domain Generalization
Natural Language Processing > Applications > Question Answering
Artificial Intelligence > Core AI > Adversarial Learning
Machine Learning > Learning Types > Domain Generalization
Deep Learning > Learning Types > Adversarial Learning
Artificial Intelligence > Learning Paradigms > Domain Generalization