Polyglot Neural Language Models: A Case Study in Cross-Lingual Phonetic Representation Learning
Table 6: Accuracies of the baseline models of lexical borrowing and the models augmented with phone vectors. In all the experiments, we use vectors from a single Polyglot LM model trained on ar, sw, fr, ro, it, mt.
['[EMPTY]', 'Accuracy (↑) ar–sw', 'Accuracy (↑) fr–ro', 'Accuracy (↑) it–mt']
We observe improvements of up to 5% in accuracies of fr– ro and it– mt pairs. Effectiveness of the same polyglot model trained on multiple languages and integrated in different downstream systems supports our assumption that the model remains stable and effective with addition of languages. Our model is less effective for the ar–sw language pair. We speculate that the results are worse, because this is a pair of (typologically) more distant languages; consequently, the phonological adaptation processes that happen in loanword assimilation are more complex than mere substitutions of similar phones that we are targeting via the integration of phone vectors.