Morphological Inflection Generationwith Hard Monotonic Attention
Table 3: Results on the SIGMORPHON 2016 morphological inflection dataset. The text above each language lists the morphological phenomena it includes: circ.=circumfixing, agg.=agglutinative, v.h.=vowel harmony, c.h.=consonant harmony
['[EMPTY]', 'suffixing+stem changes RU', 'suffixing+stem changes DE', 'suffixing+stem changes ES', 'circ. GE', 'suffixing+agg.+v.h. FI', 'suffixing+agg.+v.h. TU', 'suffixing+agg.+v.h. HU', 'c.h.', 'templatic AR', 'templatic MA', 'Avg.']
As different languages show different morphological phenomena, we also experiment with how our model copes with these various phenomena using the morphological inflection dataset from the SIGMORPHON2016 shared task Cotterell et al. We compare our model to two soft attention baselines on this dataset: med Kann and Schütze dataset our model performs better than both soft-attention baselines for the suffixing+stem-change languages (Russian, German and Spanish) and is slightly less accurate than our implementation of the soft attention model on the rest of the languages, which is now the best performing model on this dataset to our knowledge. We explain this by looking at the languages from a linguistic typology point of view, as detailed in \newcitecotterell-sigmorphon2016. Since Russian, German and Spanish employ a suffixing morphology with internal stem changes, they are more suitable for monotonic alignment as the transformations they need to model are the addition of suffixes and changing characters in the stem. The rest of the languages in the dataset employ more context sensitive morphological phenomena like vowel harmony and consonant harmony, which require to model long range dependencies in the input sequence which better suits the soft attention mechanism. While our implementation of the soft attention model and med are very similar model-wise, we hypothesize that our soft attention model results are better due to the fact that we trained the model for 100 epochs and picked the best performing model on the development set, while the med system was trained for a fixed amount of 20 epochs (although trained on more data – both train and development sets).