Graph Sequential Network for Reasoning over Sequences
Table 1: Results comparison with average and standard deviation of five runs. “ANS-only” and “SUP-only” indicate the model is only trained on two separate tasks.
['[EMPTY]', 'ANS-only EM', 'ANS-only  [ITALIC] F1', 'SUP-only EM', 'SUP-only  [ITALIC] F1']
4.2.2 Results We report the results using the best hyperparameters for each experimental setting. The results show that the proposed GSN-based models perform better on both tasks with strong statistical significance compared to the baseline GCN-based model. The improvement on EM score is slightly more significant, indicating GSN-based models are better at finding complete answer span or supporting sentences than the baseline models. When jointly training both tasks, the 2-layer GSN gives the best performance. This pattern is reasonable because the powerful BERT based encoder possibly learns good contextual representation on token level. This learned representation can benefit the answer prediction task which is also on token level, thus less graph based reasoning is required. Similarly, some recent studies Min et al. However, it is not the case for supporting sentence prediction as it requires the model to find sentences that could be far from each other in the context. On the contrast, our proposed GSN is suitable to model the relational information among sentences, thus we observe that more layers give better results for supporting sentence prediction.