Dating Documents using Graph Convolution Networks
Table 3: Accuracies of different ablated methods on the APW dataset. Overall, we observe that incorporation of context (Bi-LSTM), syntactic structure (S-GCN) and temporal structure (T-GCN) in NeuralDater achieves the best performance. Please see Section 7.1 for details.
['Method', 'Accuracy']
For demonstrating the efficacy of GCNs and BiLSTM for the problem, we evaluate different ablated variants of NeuralDater on the APW dataset. Specifically, we validate the importance of using syntactic and temporal GCNs and the effect of eliminating BiLSTM from the model. The first block of rows in the table corresponds to the case when BiLSTM layer is excluded from NeuralDater, while the second block denotes the case when BiLSTM is included. We also experiment with multiple stacked layers of T-GCN (denoted by K) to observe its effect on the performance of the model. Overall, we observe that including BiLSTM in the model improves performance significantly. Also, some gain in performance is observed on increasing the number of T-GCN layers (K) in absence of BiLSTM, although the same does not follow when BiLSTM is included in the model. We also find that eliminating edge gating from our best model deteriorates its overall performance.