FlowDelta: Modeling Flow Information Gain in Reasoningfor Conversational Machine Comprehension
Table 1: Conversational QA results on CoQA and QuAC, where (N-ctx) refers to using previous N QA pairs (%).
['[BOLD] Model', '[BOLD] CoQA  [BOLD] Dev', '[BOLD] CoQA  [BOLD] Test', '[BOLD] CoQA  [BOLD] Test', '[BOLD] QuAC  [BOLD] Dev', '[BOLD] QuAC  [BOLD] Test', '[BOLD] QuAC  [BOLD] Test', '[BOLD] QuAC  [BOLD] Test']
It can be found that FlowDeltaQA yields substantial improvement over FlowQA on both datasets (+ 0.9 % F1 on both CoQA and QuAC), showing the usefulness of explicitly modeling the information gain in the Flow layer. Furthermore, BERT-FlowDelta outperforms the published models on QuAC leaderboard on Apr 24, 2019. Specifically, while BERT-FlowDelta achieves slightly worse HEQ-Q score on QuAC to the HAM model qu2019attentive, we outperform HAM in HEQ-D metrics, showing the superiority of our model in modeling whole dialogue. Note that FlowDelta actually introduced few additional parameters compared to Flow, since it only augments the input dimension of GRU. The consistent improvement from both data demonstrates the generalization capability of applying the proposed mechanism to various models.