When and Why are Pre-trained Word Embeddings Useful for Neural Machine Translation?
Table 1: Number of sentences for each language pair.
['[BOLD] Dataset', 'train', 'dev', 'test']
In order to perform experiments in a controlled, multilingual setting, we created a parallel corpus from TED talks transcripts. (Az) and Turkish (Tr), and Belarusian (Be) and Russian (Ru). The languages in each pair are similar in vocabulary, grammar and sentence structure Matthews They also represent different language families – Gl/Pt are Romance; Az /Tr are Turkic; Be/ Ru are Slavic – allowing for comparison across languages with different caracteristics. Tokenization was done using Moses tokenizer