Cross-lingual Information Retrieval with BERT
Table 2: Performance of classification accuracy on the generated query-sentence pairs from the bitexts of the MATERIAL analysis set. The first column in the confusion matrix corresponds to the positive class (i.e., relevant query-sentence pair) while the second the column is the negative class.
['Approach BERT', 'Accuracy  [BOLD] 95.3%', 'Confusion Matrix 0.93', 'Confusion Matrix 0.07']
The QRANN and Dot-product models are trained using the same CLIR training data used to train BERT model described earlier. The CLIR BERT model achieves the best result compared to other two neural models. From the confusion matrix in the table, BERT significantly improves the performance of classifying relevant query-sentence pairs (i.e., true positives), while matching the performance of classifying irrelevant query-sentence pairs (i.e., true negatives).