Parameter Sharing Methods for Multilingual Self-Attentional Translation Models
Table 1: Number of sentences in the training, dev, and test splits for each language pair used in our experiments. The languages are represented by their ISO 639-1 codes En:English, Fr:French, Nl:Dutch, De:German, Ja:Japanese, Tr:Turkish.
['[BOLD] Language Pair', '[BOLD] Training', '[BOLD] Dev', '[BOLD] Test']
To perform multilingual translation experiments, we select six language pairs from the openly available TED talks dataset Qi et al. This dataset already contains predefined splits for training, development, and test sets. Among these languages, Romanian (Ro) and French (Fr) are Romance languages, German (De) and Dutch (Nl) are Germanic languages while Turkish (Tr) and Japanese (Ja) are unrelated languages that come from distant language families. To select training examples, we filter sentences with a maximum length of 70 tokens. For evaluation, we report the model’s performance using the standard BLEU score metric Papineni et al. We use the mtevalv14.pl script from the Moses toolkit to compute the tokenized BLEU scores.