A large annotated corpus for learning natural language inference
Bowman et al., 2015, A large annotated corpus for learning natural language inference (Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP))
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5 Conclusion
- What was SNLI’s main significance for NLP?It made it possible to train representation-learning models for sentence meaning that compete with hand-engineered NLI systems: a neural model was competitive on NLI benchmarks for the first time, and SNLI became a standard training set and testbed.
- Bowman et al., 2015, A large annotated corpus for learning natural language inference (Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP)), §5, p. 640
Source
- Samuel R. Bowman, Gabor Angeli, Christopher Potts and Christopher D. Manning. 2015. A large annotated corpus for learning natural language inference. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 632–642. doi:10.18653/v1/D15-1075 https://aclanthology.org/D15-1075/