A Decomposable Attention Model for Natural Language Inference
Ankur P. Parikh, Oscar Täckström low, Dipanjan Das, Jakob Uszkoreit
We propose a simple neural architecture for natural language inference.Our approach uses attention to decompose the problem into subproblems that can be solved separately, thus making it trivially parallelizable.On the Stanford Natural Language Inference (SNLI) dataset, we obtain state-of-the-art results with almost an order of magnitude fewer parameters than previous work and without relying on any word-order information.Adding intra-sentence attention that takes a minimum amount of order into account yields further improvements.
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- AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at S…→A Decomposable Attention Model for Natural Language InferenceLegacy edge — source detail unavailable
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding→A Decomposable Attention Model for Natural Language InferenceLegacy edge — source detail unavailable
- Natural Questions: A Benchmark for Question Answering Research→A Decomposable Attention Model for Natural Language InferenceLegacy edge — source detail unavailable
- BERTScore: Evaluating Text Generation with BERT→A Decomposable Attention Model for Natural Language InferenceLegacy edge — source detail unavailable
- Deep Learning--based Text Classification→A Decomposable Attention Model for Natural Language InferenceLegacy edge — source detail unavailable
- A Decomposable Attention Model for Natural Language Inference→Long Short-Term MemoryLegacy edge — source detail unavailable
- A Decomposable Attention Model for Natural Language Inference→Dropout: a simple way to prevent neural networks from overfittingLegacy edge — source detail unavailable
- A Decomposable Attention Model for Natural Language Inference→Glove: Global Vectors for Word RepresentationLegacy edge — source detail unavailable
- A Decomposable Attention Model for Natural Language Inference→Neural Machine Translation by Jointly Learning to Align and TranslateLegacy edge — source detail unavailable
- A Decomposable Attention Model for Natural Language Inference→Adaptive Subgradient Methods for Online Learning and Stochastic OptimizationLegacy edge — source detail unavailable
- A Decomposable Attention Model for Natural Language Inference→Deep Sparse Rectifier Neural NetworksLegacy edge — source detail unavailable
- Glove: Global Vectors for Word Representation→Adaptive Subgradient Methods for Online Learning and Stochastic OptimizationLegacy edge — source detail unavailable
- AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at S…→Glove: Global Vectors for Word RepresentationLegacy edge — source detail unavailable
- BERTScore: Evaluating Text Generation with BERT→Glove: Global Vectors for Word RepresentationLegacy edge — source detail unavailable
- BERTScore: Evaluating Text Generation with BERT→BERT: Pre-training of Deep Bidirectional Transformers for Language UnderstandingLegacy edge — source detail unavailable
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding→Glove: Global Vectors for Word RepresentationLegacy edge — source detail unavailable
- Deep Learning--based Text Classification→Glove: Global Vectors for Word RepresentationLegacy edge — source detail unavailable
- Deep Learning--based Text Classification→AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at S…Legacy edge — source detail unavailable
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| Topic Modeling | Computer Science |
| Natural Language Processing Techniques | Computer Science |
| Multimodal Machine Learning Applications | Computer Science |
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