Contrastive Analysis with Predictive Power: Typology Driven Estimation of Grammatical Error Distributions in ESL
Table 6: Results for prediction of relative error frequencies using the bootstrapping approach. In this setup, the true typology of the test language is substituted with approximate typology derived from morpho-syntactic ESL features.
['[EMPTY]', '[BOLD] Base', '[BOLD] NN', '[BOLD] Reg', '[BOLD] RegCA']
As can be seen, our approach continues to provide substantial performance gains despite the inaccuracy of the typological information used for the test languages. The best performing method, RegCA reduces the MAE of Base by 13.9%, with performance improvements for most of the languages and error types. Performance gains are also obtained on the DKL metric, whereby RegCA scores 0.041, compared to the Base score of 0.052, improving on 11 out of our 14 languages.