A Simple but Effective Method to Incorporate Multi-turn Context with BERT for Conversational Machine Comprehension
Table 3: The results with the number of previous contexts on the development set of QuAC and CoQA (F1 score)
['[EMPTY]', '# contexts', 'CoQA', 'QuAC']
Does our model improve the performance when the number of previous contexts increases? On both of the datasets, it was effective to use previous contexts. However, on CoQA, the number of contexts had little effect on the score even if the long context was considered. On QuAC, the best score was obtained in the case of using two contexts, and the score decreased with more than two contexts. Thus, the previous context on QuAC can include the context that is unrelated to the current question, and this unrelated context can decrease the score. This result suggests that it is important to select context that is related to the current question and not use the whole context in any cases.