Scalable Cross Lingual Pivots to Model Pronoun Gender for Translation
Table 7: Even accounting for difference in the raw number of pronouns by gender, prodrop is more likely for masculine entities than feminine entities in WMT’13 (development).
['[EMPTY]', 'Count', 'Prodrop', 'Rate']
Performance is weak overall but especially on feminine examples of prodrop. The tendency is for masculine pronouns to be over-used: feminine pronouns are used by Baseline MT (Sentences) less than one time in five when they should be. Specifically, we counted the number of times the English pronouns he and she occurred (first column). Not surprisingly, there was an over-representation of masculine pronouns; for each feminine example in the data, there was four masculine examples. Next, we looked in the aligned Spanish sentences for the corresponding pronouns él and ella and counted a case of prodrop each time they were missing (second column). Even accounting for differences in raw frequency by gender, there is a difference in which prodrop is more frequent for masculine entities. This is the first time this difference has been reported and future work should control for the prior it induces.