Fine-Grained Prediction of Syntactic Typology: Discovering Latent Structure with Supervised Learning
Table 8: Our final comparison on the 17 test languages appears at left. We ask whether the average expected loss on these 17 real target languages is reduced by augmenting the training pool of 20 UD languages with +20*21*21 GD languages. For completeness, we extend the table with the cross-validation results on the training pool. The “Avg.” lines report the average of 17 test or 37 training+testing languages. We mark both “+GD” averages with “*” as they are significantly better than their “UD” counterparts (paired permutation test by language, p<0.05).
['Test target', 'Test UD', 'Test +GD', 'Train target', 'Train UD', 'Train +GD']
All previous experiments were conducted by cross-validation on the 20 training languages. The results range from good to excellent, with synthetic data providing consistent and often large improvements. This holds promise for aiding grammar induction.