A Structured Variational Autoencoderfor Contextual Morphological Inflection
Table 3: Type-level morphological inflection accuracy across different models, training scenarios, and languages
['[EMPTY]', '[EMPTY]', '[BOLD] lang', '500 tokens FST', '500 tokens NN', '500 tokens SVAE', '500 tokens  [BOLD] Δ [ITALIC] FST', '500 tokens  [BOLD] Δ [ITALIC] NN', '1000 tokens FST', '1000 tokens NN', '1000 tokens SVAE', '1000 tokens  [BOLD] Δ [ITALIC] FST', '1000 tokens  [BOLD] Δ [ITALIC] NN', '5000 tokens FST', '5000 tokens NN', '5000 tokens SVAE', '5000 tokens  [BOLD] Δ [ITALIC] FST', '5000 tokens  [BOLD] Δ [ITALIC] NN']
We explore a typologically diverse set of languages of various stocks: Indo-European, Afro-Asiatic, Turkic and Finno-Ugric, as well as the language isolate Basque. The Indo-European languages mostly exhibit fusional morphologies of varying degrees of complexity. The Basque, Turkic, and Finno-Ugric languages are agglutinative. Both of the Afro-Asiatic languages, Arabic and Hebrew, are Semitic and have templatic morphology with fusional affixes. The high-level take-away is that on almost all languages we are able to exploit the unlabeled data to improve the sequence-to-sequence model using unlabeled data , i.e., SVAE outperforms the NN model on all languages across all training scenarios. However, in many cases, the FST model is a better choice—the FST can sometimes generalize better from a handful of supervised examples than the neural network, even with semi-supervision (SVAE). We highlight three finer-grained observations below. The final experiments were produced with the follow setting. We performed a modest grid search over various configurations in the search of the best option on development for each component.