Top-down Tree Long Short-Term Memory Networks
Table 2: Performance of TreeLSTM and LdTreeLSTM on reranking the top dependency trees produced by the 2nd order MSTParser [McDonald and Pereira2006]. Results for the NN and S-LSTM parsers are reported in chen-manning:2014:EMNLP2014 and dyer-EtAl:2015:ACL-IJCNLP, respectively. * indicates that the model is initialized with pre-trained word vectors.
['[BOLD] Parser', 'Development UAS', 'Development LAS', 'Test UAS', 'Test LAS']
(d=300, 1 layer, K=2), and LdTreeLSTM (d=200, 2 layers, K=4). We also include the performance of two neural network-based dependency parsers; \newcitechen-manning:2014:EMNLP2014 use a neural network classifier to predict the correct transition (NN parser); \newcitedyer-EtAl:2015: ACL-IJCNLP also implement a transition-based dependency parser using LSTMs to represent the contents of the stack and buffer in a continuous space. As can be seen, both TreeLSTM and LdTreeLSTM outperform the baseline MSTParser, with LdTreeLSTM performing best. We obtained a slight improvement over TreeLSTM (TreeLSTM* but no improvement over LdTreeLSTM. Finally, notice that LdTreeLSTM is slightly better than the NN parser in terms of UAS but worse than the S-LSTM parser. In the future, we would like to extend our model so that it takes labeled dependency information into account.