Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks
Table 2: Test set accuracies on the Stanford Sentiment Treebank. For our experiments, we report mean accuracies over 5 runs (standard deviations in parentheses). Fine-grained: 5-class sentiment classification. Binary: positive/negative sentiment classification.
['[BOLD] Method', '[BOLD] Fine-grained', '[BOLD] Binary']
The Constituency Tree-LSTM outperforms existing systems on the fine-grained classification subtask and achieves accuracy comparable to the state-of-the-art on the binary subtask. In particular, we find that it outperforms the Dependency Tree-LSTM. This performance gap is at least partially attributable to the fact that the Dependency Tree-LSTM is trained on less data: about 150K labeled nodes vs. 319K for the Constituency Tree-LSTM. This difference is due to (1) the dependency representations containing fewer nodes than the corresponding constituency representations, and (2) the inability to match about 9% of the dependency nodes to a corresponding span in the training data.