Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension
Table 5: Distribution (%) of types of required prior knowledge based on a subset of test and development sets of C{}^{3}, Chinese free-form abstractive dataset DuReader he2017dureader, and English free-form multiple-choice datasets RACE lai2017race and DREAM sundream2018. Answering a question may require more than one type of prior knowledge.
['[EMPTY]', '{\\text{C}}_{\\text{M}}^{3}', '{\\text{C}}_{\\text{D}}^{3}', '[BOLD] C{}^{3}', '[BOLD] RACE', '[BOLD] DREAM', '[BOLD] DuReader']
As many as 24.3\% questions in {\text{C}}_{\text{D}}^{3} need scenario knowledge perhaps due to that speakers in a dialogue (especially face-to-face) may not explicitly mention information that they assume others already know such as personal information, the relationship between the speakers, and temporal and location information. Interestingly, we observe a similar phenomenon when we compare the English datasets DREAM (dialogue-based) and RACE. Therefore, it is likely that dialogue-based free-form tasks can serve as ideal platforms for studying how to improve language understanding with general world knowledge regardless of language.