Question Answering from Unstructured Text by Retrieval and Comprehension
Table 7: Hits@1 scores for each question type. Our model gets >80% in all cases but two.
['[BOLD] Question Type', '[BOLD] KV', '[BOLD] r1+AsV']
We can see that performance improvements in the movie_to_x category is relatively large. The KV-MemNN model has a dataset specific “Title encoding” feature which helps the model x_to_movie question types. However without this feature performance in other categories is poor.