Learning Contextualized Document Representations for Healthcare Answer Retrieval
Table 3. Results for the answer passage retrieval task on three healthcare datasets. All models were trained using the self-supervised Wikipedia training set and applied without fine-tuning. Queries were evaluated by ranking 64 given candidates from the respective test sets. As queries we used ⟨entity,aspect⟩ tuples in a representation suitable for the individual model.
['[BOLD] Model all trained on Wikipedia', '[BOLD] WikiSectionQA  [ITALIC] R@1', '[BOLD] WikiSectionQA  [ITALIC] R@10', '[BOLD] WikiSectionQA MAP', '[BOLD] MedQuAD  [ITALIC] R@1', '[BOLD] MedQuAD  [ITALIC] R@10', '[BOLD] MedQuAD MAP', '[BOLD] HealthQA  [ITALIC] R@1', '[BOLD] HealthQA  [ITALIC] R@10', '[BOLD] HealthQA MAP']
We observe that CDV consistently achieves significantly better results than all term-based, representation-based and combined models across all datasets. In comparison with pairwise interaction-based models, our representation-based retrieval model outperforms all tested models on average, scores best on WikiSection and HealthQA and second best on the MedQuAD dataset. Retrieval time per query is 247ms (±43ms) on average.