Question Answering from Unstructured Text by Retrieval and Comprehension
Table 4: Performance of scoring models
['[BOLD] Model Type', '[BOLD] R@1', '[BOLD] R@10', '[BOLD] R@30', '[BOLD] R@100', '[BOLD] P@1', '[BOLD] P@10', '[BOLD] P@30', '[BOLD] P@100']
We have also tested simpler models based on inner product of question and article vectors. In these models, a question qj and article dk are converted to vectors Φ(qj),Ψ(dk), and the relevance score is given by their inner product: score(j,k)=Φ(qj)TΨ(dk). (11) In the view of computation, those models are attractive because we can compute the article vectors offline, and do not need to compute the attention over words in the article. Maximum Inner Product Search algorithms may also be utilized here Chandar et al. ; Auvolat et al. The “Sum of Hidden State” and “Query Free Attention” models are similar to WLA model, using BiGRUs for question and article. For the “Sum of Hidden States” model, Ψ(d) is given by the sum of BiGRU hidden states. This is the same as the proposed model by replacing the fourth order of WLA to one. For the “Query Free Attention” model, Ψ(d) is given by the sum of BiGRU hidden states.