MLQA: Evaluating Cross-lingual Extractive Question Answering
Table 6: F1 Score for XLM for G-XLT. Columns show question language, rows show context language.
['c/q', '[BOLD] en', '[BOLD] es', '[BOLD] de', '[BOLD] ar', '[BOLD] hi', '[BOLD] vi', '[BOLD] zh']
For questions in a given language, the model performs best when the context language matches the question, except for Hindi and Arabic. For contexts in a given language, English questions tend to perform best, apart from Chinese and Vietnamese. Multilingual BERT exhibits more of a preference for English than XLM for G-XLT, and exhibits a bigger performance drop going from XLT to G-XLT (10.5 mean drop in F1 compared to 8.2).