Large Scale Question Answering using Tourism Data
Table 10: Curriculum learning (CL) with different entity embedding schemes (Full-ranking task)
['[BOLD] Method', '[BOLD] Acc@3(%)', '[BOLD] Acc@5(%)', '[BOLD] Acc@30 (%)', '[BOLD] MRR']
We also experiment with two baseline methods of creating entity embeddings: (i) Using the averaged sentence embeddings of the representative documents (AVG. (ii) Doc2Vec doc2vec. We employ curriculum learning, slowly increasing the selection probability of hard negatives up to a maximum of 0.6. Interestingly, the negative samples from Duet DUET; DUET2 results in comparable performance but using Duet as selection mechanism results in significantly improved performance as shown in the main paper. The CrQA model described in the main paper using the task specific embeddings (last row) for training.