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Natural Language Inference On Scitail

Metrics

Dev Accuracy

Results

Performance results of various models on this benchmark

Model Name
Dev Accuracy
Paper TitleRepository
MT-DNN-SMART_1%ofTrainingData88.6SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization
Finetuned Transformer LM-Improving Language Understanding by Generative Pre-Training
RE2-Simple and Effective Text Matching with Richer Alignment Features
MT-DNN-SMARTLARGEv0-SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization
SplitEE-S-SplitEE: Early Exit in Deep Neural Networks with Split Computing
CA-MTL-Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters & Less Data
Hierarchical BiLSTM Max Pooling-Sentence Embeddings in NLI with Iterative Refinement Encoders
MT-DNN-Multi-Task Deep Neural Networks for Natural Language Understanding
MT-DNN-SMART_0.1%ofTrainingData82.3SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization
MT-DNN-SMART_100%ofTrainingData96.1SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization
CAFE-Compare, Compress and Propagate: Enhancing Neural Architectures with Alignment Factorization for Natural Language Inference-
MT-DNN-SMART_10%ofTrainingData91.3SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization
Finetuned Transformer LM---
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