Cross-Lingual Machine Reading Comprehension
Table 2: Experimental results on CMRC 2018 and DRCD. † indicates unpublished works (some of the systems are using development set for training, which makes the results not directly comparable.). ♠ indicates zero-shot approach. We mark our system with an ID in the first column for reference simplicity.
['[BOLD] #', '[BOLD] System', '[BOLD] CMRC 2018  [BOLD] Dev', '[BOLD] CMRC 2018  [BOLD] Dev', '[BOLD] CMRC 2018  [BOLD] Test', '[BOLD] CMRC 2018  [BOLD] Test', '[BOLD] CMRC 2018  [BOLD] Challenge', '[BOLD] CMRC 2018  [BOLD] Challenge', '[BOLD] DRCD  [BOLD] Dev', '[BOLD] DRCD  [BOLD] Dev', '[BOLD] DRCD  [BOLD] Test', '[BOLD] DRCD  [BOLD] Test']
As we can see that, without using any alignment approach, the zero-shot results are quite lower regardless of using English BERT-base (#1) or BERT-large (#2). When we apply SimpleMatch (#3), we observe significant improvements demonstrating its effectiveness. The Answer Aligner (#4) could further improve the performance beyond SimpleMatch approach, demonstrating that the machine learning approach could dynamically adjust the span output by learning the semantic relationship between translated answer and target passage. Also, the Answer Verifier (#5) could further boost performance and surpass the multi-lingual BERT baseline (#7) that only use target training data, demonstrating that it is beneficial to adopt rich-resourced language to improve machine reading comprehension in other languages.