Learning to Attend On Essential Terms: An Enhanced Retriever-Reader Model for Open-domain Question Answering
Table 9: Accuracy on multiple-choice selection on three product categoris of Amazon-QA.
['Model', 'Amazon -Patio', 'Amazon -Auto', 'Aamzon -Cell']
On the RACE-Open and MCScript-Open datasets, ET-RR achieves a relative improvement of 24.6% and 10.5% on the test set compared with the second best method IR solver respectively. We also evaluate on multiple categories of the Amazon-QA dataset. We also compare ET-RR with ET-RR (Concat), which is a variant of our proposed model that concatenates the question and choice as a query for each choice. Among all datasets, ET-RR outperforms ET-RR (concat) consistently which shows the effectiveness of our essential-term-aware retriever.