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))
Subscriber deck — free during early access
1 cards in “4 Transfer learning with SICK” · whole deck
4 Transfer learning with SICK
- How did SNLI help on the smaller SICK entailment task?An LSTM trained on SNLI and then fine-tuned on SICK (new softmax and embeddings for unseen words, AdaDelta accumulators transferred to lower learning rates) reached 80.8%, the best reported for an unaugmented neural model, vs 71.3% trained on SICK alone and 46.7% for SNLI without fine-tuning; the state of the art was 84.6%.
- 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)), §4, pp. 639–640
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/