Utilizing BERT Intermediate Layers for Aspect Based Sentiment Analysis and Natural Language Inference
Table 3: Classification accuracy (%) for natural language inference on SNLI dataset. Results with “*” are obtained from the official SNLI leaderboard (https://nlp.stanford.edu/projects/snli/).
['[BOLD] Model', 'Dev', 'Test']
From the results, BERT-Attention and BERT-LSTM perform better than vanilla BERT\textscBASE. Furthermore, MT-DNN-Attention and MT-DNN-LSTM outperform vanilla MT-DNN on Dev set, and are slightly inferior to vanilla MT-DNN on Test set. As a whole, our pooling strategies generally improve the vanilla BERT-based model, which draws the same conclusion as on ABSA.