Enhancing lexical-based approach with external knowledge for Vietnamese multiple-choice reading comprehension
Table 4: Experimental results of different models with various pre-trained word embeddings on our corpus ViMMRC.
['Model group', 'Method', 'Dev (%)', 'Test (%)']
Sliding Window and Distance-based Sliding Window achieve different performances, 58.50% and 60.55%, on the development set but they have the same accuracy of 56.30% on the test set. Our proposed method achieves the accuracies over 60% on the test set and over 61% on the development set. Specifically, this method with the ELMO word embedding achieves the highest results on both of the test and development sets, 65.99% and 61.81%, respectively. This proves that our proposed method is more effective than the other methods for the Vietnamese MMRC task at present. It can be seen that the results are influenced by the methods when combined with these word embeddings. In particular, both of the lexical-based method and the neural-based method have better results when using word embeddings, approximately 5% higher. The experimental results showed that Elmo is the best among the other word embeddings.