Bidirectional Tree-Structured LSTM with Head Lexicalization
Table 5: Effect of model size.
['Model', '| [ITALIC] h|', '#Parameter', 'Accuracy (%)']
We did not count the number of parameters in the lookup table since these parameters are the same for all models. Since the size of LSTM models mainly depends on the dimensionality of the state vector h, we change the size of h for comparing the effect of model size. When |h|=150, the model size of the baseline model ConTree is the smallest, which consists of about 538K parameters. The model size of ConTree+Lex is about 1.4 times as large as that of the baseline model. The bidirectional model BiConTree is the largest, which is about 1.7 times as large as that of the ConTree+Lex model. However, this parameter set is not very large compared to the modern memory capacity, even for a computer with 16GB RAM. In conclusion, in terms of both time, model size and accuracy, head lexicalization method is a good choice. For the same model, increasing the model size can improve the performance to some extent. For example, doubling the size of |h| (75→150) increases the performance from 51.5 to 52.8 for the ConTree+Lex model. Similarly, we boost the performance of the BiConTree model when doubling the size of |h| from 75 to 150. However, continuing doubling the size of |h| from 150 to 300 empirically decreases the performance of the ConTree+Lex model. The BiConTree model with |h|=75 is much smaller than the ConTree+Lex model with |h|=150 in terms of model size. However the performance of these two models is quite close, which indicates that top-down information is useful even for a small model. We also run a ConTree+Lex model with |h|=215, the model size of which is similar to that of the BiConTree model with |h|=150. The performance of the ConTree+Lex model is still worse than the BiConTree model (52.5 v.s. 53.5), which shows the effectiveness of top-down information.