Question: What source-target language pairs were used in this work? 

Evidence:
raw_evidence: FLOAT SELECTED: Table 2: EM/F1 score of multi-BERTs fine-tuned on different training sets and tested on different languages (En: English, Fr: French, Zh: Chinese, Jp: Japanese, Kr: Korean, xx-yy: translated from xx to yy). The text in bold means training data language is the same as testing data language.
raw_evidence: In the lower half of Table TABREF8, the results are obtained by the translated training data. First, we found that when testing on English and Chinese, translation always degrades the performance (En v.s. En-XX, Zh v.s. Zh-XX). Even though we translate the training data into the same language as testing data, using the untranslated data still yield better results. For example, when testing on English, the F1 score of the model training on Chinese (Zh) is 53.8, while the F1 score is only 44.1 for the model training on Zh-En. This shows that translation degrades the quality of data. There are some exceptions when testing on Korean. Translating the English training data into Chinese, Japanese and Korean still improve the performance on Korean. We also found that when translated into the same language, the English training data is always better than the Chinese data (En-XX v.s. Zh-XX), with only one exception (En-Fr v.s. Zh-Fr when testing on KorQuAD). This may be because we have less Chinese training data than English. These results show that the quality and the size of dataset are much more important than whether the training and testing are in the same language or not.
FLOAT SELECTED: Table 2: EM/F1 score of multi-BERTs fine-tuned on different training sets and tested on different languages (En: English, Fr: French, Zh: Chinese, Jp: Japanese, Kr: Korean, xx-yy: translated from xx to yy). The text in bold means training data language is the same as testing data language.
raw_evidence: We have training and testing sets in three different languages: English, Chinese and Korean. The English dataset is SQuAD BIBREF2. The Chinese dataset is DRCD BIBREF14, a Chinese RC dataset with 30,000+ examples in the training set and 10,000+ examples in the development set. The Korean dataset is KorQuAD BIBREF15, a Korean RC dataset with 60,000+ examples in the training set and 10,000+ examples in the development set, created in exactly the same procedure as SQuAD. We always use the development sets of SQuAD, DRCD and KorQuAD for testing since the testing sets of the corpora have not been released yet.
Next, to construct a diverse cross-lingual RC dataset with compromised quality, we translated the English and Chinese datasets into more languages, with Google Translate. An obvious issue with this method is that some examples might no longer have a recoverable span. To solve the problem, we use fuzzy matching to find the most possible answer, which calculates minimal edit distance between translated answer and all possible spans. If the minimal edit distance is larger than min(10, lengths of translated answer - 1), we drop the examples during training, and treat them as noise when testing. In this way, we can recover more than 95% of examples. The following generated datasets are recovered with same setting.

Answers:
En-Fr, En-Zh, En-Jp, En-Kr, Zh-En, Zh-Fr, Zh-Jp, Zh-Kr to English, Chinese or Korean
English , Chinese
English, Chinese, Korean, we translated the English and Chinese datasets into more languages, with Google Translate