A Latent Morphology Model for Open- Vocabulary Neural Machine Translation
Table 1: Above: Machine translation accuracy in Arabic (AR), Czech (CS) and Turkish (TR) in terms of BLEU and ChrF3 metrics as well as BLEU scores computed on the output sentences tagged with the morphological analyzer (t-BLEU) using in-domain training data. Below: The performance of models trained with multi-domain data. Best scores are in bold. All improvements over the baselines are statistically significant (p-value 
['[BOLD] Model', '[ITALIC] (only in-domain)  [BOLD] AR', '[ITALIC] (only in-domain)  [BOLD] AR', '[ITALIC] (only in-domain)  [BOLD] AR', '[ITALIC] (only in-domain)  [BOLD] CS', '[ITALIC] (only in-domain)  [BOLD] CS', '[ITALIC] (only in-domain)  [BOLD] CS', '[ITALIC] (only in-domain)  [BOLD] TR', '[ITALIC] (only in-domain)  [BOLD] TR', '[ITALIC] (only in-domain)  [BOLD] TR']
4.4.1 The Effect of Morphological Typology In Turkish, the most sparse target language in our benchmark with rich agglutinative morphology, using character-based decoding shows to be more advantageous compared to the subword-level and hierarchical models, suggesting that increased granularity in the vocabulary units might aid in better learning accurate representations under conditions of high data sparsity. In Arabic, on the other hand, using a hierarchical decoding model shows to be advantageous compared to the subword and character-level models, as it might be useful in better learning syntactic dependencies. LMM obtains improvements of 0.51 and 0.30 BLEU points in Arabic and Turkish over the best performing baselines, respectively. The fact that our model can efficiently work in both Arabic and Turkish confirms that it can handle the generation of both concatenative and non-concatenative morphological transformations. The results in the English-to-Czech translation direction do not indicate a specific advantage of using either method for generating fusional morphology, where morphemes are already optimized at the surface level, although our model is still able to achieve translation accuracy comparable to the character and subword-level models.