DREAM: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension
Table 11: Accuracy (%) by question type on the annotated development subset.
['[BOLD] Question Type', '[BOLD] FTLM++', '[BOLD] GBDT++']
Impact of Question Types Not surprisingly, both models perform worse than random guessing on math problems. While most of the problems can be solved by one single linear equation, it is still difficult to apply recent neural math word problem solvers Huang et al. For example, given the dialogue: “W: The plane arrives at 10:50. It is already 10:40 now. Be quick! M: Relax. Your watch must be fast. There are still twenty minutes left.” , we need prior knowledge to infer that the watch of the man is showing incorrect time 10:40. Instead, 10:50 should be used as the reference time with the time interval “twenty minutes left” together to answer the question “What time is it now?”.