Crossing Variational Autoencoders for Answer Retrieval
Table 2: Performance of answer retrieval on SQuAD.
['[BOLD] Method', '[BOLD] SQuAD MRR', '[BOLD] SQuAD R@1', '[BOLD] SQuAD R@5']
Comparing performance with baselines. In contrast, using BERT token embedding can perform better in our retrieval task. Our proposed method outperforms all baseline methods. Comparing with USE-QA, our method improves MRR and R@1 by +1.06% and +2.44% on SQuAD, respectively. In addition, Dual variational autoencoder (Dual-VAEs) does not make much improvement on question answering retrieval task because it can only preserve isolated semantics of themselves. Our proposed crossing variational autoencoder (Cross-VAEs) could outperform dual-encoder model and dual variational autoencoder model, which improves MRR and R@1 by +1.23%/+0.81% and +0.90%/+0.59%, respectively.