Learning to Learn Morphological Inflection for Resource-Poor Languages
Table 2: Inflection accuracy on the test languages; best results in bold. Languages listed by language family; from top to bottom: Romance, Slavic, Uralic.
['[EMPTY]', '[BOLD] PG  [BOLD] MAML-PG', '[BOLD] PG  [BOLD] MulPG+FT', '[BOLD] PG  [BOLD] MulPG', '[BOLD] PG', '[BOLD] MED  [BOLD] MAML-MED', '[BOLD] MED  [BOLD] MulMED+FT', '[BOLD] MED  [BOLD] MulMED', '[BOLD] UZH']
We make the following observations: For both MED and PG, MAML-trained models outperform all other models of the same architecture: MAML-PG (resp. MAML-MED) obtains a 1.21% (resp. 3.16%) higher accuracy on average over languages than the second best model MulPG+FT (resp. MulMED+FT). This demonstrates that MAML is more effective than multi-task training. For both architectures, models which are obtained by multi-task training and subse1uent fine-tuning outperform models trained exclusively in a multi-task fashion: MulPG+FT (resp. MulMED+FT) obtains a 10.41% (resp. 19.06%) higher accuracy on average than MulPG (resp. MulMED). Since differences in performance are substantial, we conclude that the use of fine-tuning—with or without MAML—is important. All PG models outperform their corresponding MED models. This is in line with the findings of \citeauthorcotterell-EtAl:2018:K18 -30 \shortcitecotterell-EtAl:2018:K18-30 that the pointer-generator network is a strong model for morphological inflection in the low-resource setting. Plain PG performs worse than fine-tuned PG models, but better than MulPG. That both MAML-PG and MulPG+FT outperform PG shows the importance of fine-tuning. Multi-task training as suggested by \citeauthorkann-cotterell-schutze:2017: Long \shortcitekann-cotterell-schutze:2017: Long seems to not work well in our setup. One possible reason for this is that we include languages from 3 different families as opposed to the original experiments which focussed on one family at a time. Thus, better ways to account for the amount of unrelated examples are probably needed for MulPG. MulPG+FT and MAML-PG perform better than UZH, the state-of-the-art model. We conclude that cross-lingual transfer is a promising direction to improve morphological inflection for resource-poor languages. Looking at individual languages, MAML-PG performs better than the plain PG model in all cases, except for 3 Romance (Ladin, Norman, and Venetian) and 3 Uralic (Hungarian, Karelian, and Livonian) languages. There are no exceptions for the Slavic family. That relatively many monolingual models outperform cross-lingual transfer models for Uralic languages might be due to these languages being less similar than those in the other families. We look into this in more detail in the next section.