Question Answering from Unstructured Text by Retrieval and Comprehension
Table 5: Performance (hits@1) comparison over different models and datasets.
['[EMPTY]', 'WikiMovies-WE r0', 'WikiMovies-WE r1', 'WikiMovies-WE R2', 'WikiMovies-FL r0', 'WikiMovies-FL r1', 'WikiMovies-FL R2']
We compare our model and several ablations with the KV-MemNN model. The (V) “Vocabulary Model” and (A) “Attention Model” are simplified versions of the full (AV) “Attention and Vocabulary Model”, using only pvocab and patt, respectively. Using a mixture of patt and pvocab gives the best performance. We can see that improving the quality of retrieved articles benefits the downstream comprehension performance. In line with the results of the previous section, (r1) and (R2) significantly outperform (r0). Among (r1) and (R2), (R2) performs slightly better.