On Tree-Based Neural Sentence Modeling
Table 2: Test results for different encoder architectures trained by a unified encoder-classifier/decoder framework. We report accuracy (×100) for classification tasks, and BLEU score (Papineni et al., 2002; word-level for English targets and char-level for Chinese targets) for generation tasks. Large is better for both of the metrics. The best number(s) for each task are in bold. In addition, average sentence length (in words) of each dataset is attached in the last row with underline.
['[BOLD] Model', '[ITALIC] Sentence Classification  [BOLD] AGN', '[ITALIC] Sentence Classification  [BOLD] ARP', '[ITALIC] Sentence Classification  [BOLD] ARF', '[ITALIC] Sentence Classification  [BOLD] DBpedia', '[ITALIC] Sentence Classification  [BOLD] WSR', '[ITALIC] Sentence Relation  [BOLD] NLI', '[ITALIC] Sentence Relation  [BOLD] Conj', '[ITALIC] Sentence Generation  [BOLD] Para', '[ITALIC] Sentence Generation  [BOLD] MT', '[ITALIC] Sentence Generation  [BOLD] AE']
However, left-branching tree LSTM has a quite similar structure with linear LSTM, and it can be viewed as a linear LSTM-on-LSTM structure. It has the same amounts of parameters as other tree-based encoders, but still falls behind the balance tree encoder on most of the tasks. This indicates that larger quantity of parameters is at least not the only reason for binary balance tree LSTM encoders to gain improvements against bi-LSTMs.