Crossing Variational Autoencoders for Answer Retrieval
Table 3: Performance of answer retrieval on a subset of SQuAD in which any answer has more than 8 questions. Our method outperforms baselines much more. SSE indicates the sum of squared distances/errors between two different questions aligned to same answer.
['[BOLD] Method', '[BOLD] SQuAD Subset MRR', '[BOLD] SQuAD Subset R@1', '[BOLD] SQuAD Subset R@5', '[BOLD] SQuAD Subset SSE']
Analyzing performance on sub-dataset. We extract a subset of SQuAD, in which any answer has at least eight different questions. Our method improves MRR and R@1 by +1.46% and +3.65% over USE-QA. Cross-VAEs significantly improve the performance when an answer has multiple aligned questions. Additionally, SSE of our method is smaller than that of USE-QA. Therefore, the questions of the same answer are closer in the latent space.