2018
EMNLP
EMNLP 2018
Convolutional Interaction Network for Natural Language Inference
Abstract
AbstractAttention-based neural models have achieved great success in natural language inference (NLI). In this paper, we propose the Convolutional Interaction Network (CIN), a general model to capture the interaction between two sentences, which can be an alternative to the attention mechanism for NLI. Specifically, CIN encodes one sentence with the filters dynamically generated based on another sentence. Since the filters may be designed to have various numbers and sizes, CIN can capture more complicated interaction patterns. Experiments on three large datasets demonstrate CINβs efficacy.
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Interdisciplinary Bridge
β Computer Science and Deep Learning and Natural Language Processing
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Keyword Pioneer
β interaction pattern
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Cross-Pollinator
β 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
Deep Learning > Architectures > Neural Networks
Computer Science > Applications > Document Analysis
Natural Language Processing > Applications > Natural Language Inference
Deep Learning > Learning Types > Representation Learning
Deep Learning > Techniques > Attention
Deep Learning > Architectures > Convolutional Neural Networks