Comments (4)
I'm sure that the difference is large on Chinese.
Before multilingual question answering dataset released, I do some zero-shot reading comprehension experiment on DRCD (Chinese) and KorQuAD (Korean).
The result is reported in https://arxiv.org/pdf/1909.09587.pdf.
If the evaluation script is modified as here, I got 'exact': 66.71396140749148, 'f1': 78.41471541616556
on DRCD. Without modified, I got "exact": 66.71396140749148, "f1": 66.71396140749148,
on DRCD.
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Scores before changing the script
XQuAD
en {"exact_match": 69.15966386554622, "f1": 81.326960111499}
es {"exact_match": 50.84033613445378, "f1": 71.9177204984632}
de {"exact_match": 49.15966386554622, "f1": 66.40521293685734}
el {"exact_match": 31.428571428571427, "f1": 47.04197672432378}
ru {"exact_match": 51.34453781512605, "f1": 68.7757929521295}
tr {"exact_match": 29.831932773109244, "f1": 45.22649443514344}
ar {"exact_match": 43.94957983193277, "f1": 60.508341054177116}
vi {"exact_match": 13.193277310924369, "f1": 31.339180641908623}
th {"exact_match": 18.65546218487395, "f1": 27.48267185662145}
zh {"exact_match": 48.90756302521008, "f1": 58.35407496331861}
hi {"exact_match": 26.386554621848738, "f1": 41.85833342624981}
Scores after changing the script
XQuAD
en {"exact_match": 69.15966386554622, "f1": 81.12936130528962}
es {"exact_match": 50.84033613445378, "f1": 70.39142998082033}
de {"exact_match": 49.15966386554622, "f1": 65.50942887082724}
el {"exact_match": 31.428571428571427, "f1": 56.924266644045474}
ru {"exact_match": 51.34453781512605, "f1": 73.58736598799115}
tr {"exact_match": 29.831932773109244, "f1": 47.694480985740995}
ar {"exact_match": 43.94957983193277, "f1": 69.68323791997037}
vi {"exact_match": 13.193277310924369, "f1": 38.22218477944847}
th {"exact_match": 18.65546218487395, "f1": 41.452670791585874}
zh {"exact_match": 48.90756302521008, "f1": 66.19815379584597}
hi {"exact_match": 26.386554621848738, "f1": 52.00823001166374}
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Thanks for the note. I had experimented with using the MLQA evaluation script for XQuAD but only observed marginal differences in some experiments (as mentioned here). If the differences are indeed larger, we might consider updating the evaluation script. What model did you use to obtain the scores?
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Hi @sebastianruder, I used bert-base-multilingaul-cased
.
However, as I mentioned in #8 (comment), there is a bug in scripts/*_qa.sh.
In the abovementioned result, I do not remove --do_lower_case argument.
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