Graph Sequential Network for Reasoning over Sequences
Table 2: Results comparison with average and standard deviation of five runs. “ANS”, “SUP” and “JOINT” indicate the jointly trained models’ performance in terms of measurements on answer span prediction, supporting sentence prediction and joint tasks.
['[EMPTY]', 'ANS EM', 'ANS  [ITALIC] F1', 'SUP EM', 'SUP  [ITALIC] F1', 'JOINT EM', 'JOINT  [ITALIC] F1']
Compared to the baseline model, more improvement is attained than models trained only on one task, especially for the supporting sentence prediction task. With joint training, the performance on answer span prediction drops while the performance on supporting sentence prediction increases. Actually we observed better joint EM and F1 scores with joint training compared to separate training for both baseline models and GSN-based models. Thus, joint training still boosts the performance overall, because the joint trained models find the correct answer and supporting sentences of a question simultaneously.