Cross-Lingual Transfer Learning for Question Answering
Table 7: EM/F1 test-on-source scores over DRCDfilter (English) and DRCD (English). Because the testing documents are translated into English, the output predictions of the QA model are also in English (column (1), column (3)). We also back-translated the English predictions into Chinese (column (2), column (4)) and evaluated with ground-truths from DRCDfilter and DRCD respectively.
['[EMPTY]', '[EMPTY]', 'DRCD [ITALIC] filter (English) (1) ENG Ans', 'DRCD [ITALIC] filter (English) (1) ENG Ans', 'DRCD [ITALIC] filter (English) (2) CH Ans', 'DRCD [ITALIC] filter (English) (2) CH Ans', 'DRCD (English) (3) ENG Ans', 'DRCD (English) (3) ENG Ans', 'DRCD (English) (4) CH Ans', 'DRCD (English) (4) CH Ans']
For the test-on-source approach, all the Chinese examples in DRCD, including the training and testing sets, were transcribed into English and they are denoted as DRCD (English). This was expected because the Chinese predictions were translated from the English predictions; the resulting translation errors degraded performance. However, when comparing the different approaches, the conclusion drawn from the Chinese and English evaluations is the same: They both show that training from DRCD (English) outperforms SQuAD even though SQuAD has more training examples. This is intuitive because the data distribution of DRCD (English) is closer to the testing data here. Using both corpora is even better (row (e) vs. row (d)).