Diversity by Phonetics and its Application in Neural Machine Translation
Table 8: Robustness in BLEU[%]. Train: IWSLT’17 EN-FR adding noisy augmented data; Test: IWSLT’17 test by varying edit distance.
['Source Data', 'No. of Operations 0', 'No. of Operations 1', 'No. of Operations 2', 'No. of Operations 3', 'No. of Operations 4', '5']
We test the system robustness with noise augmented data on IWSLT’17 EN-FR. In training, we randomly sample 20% of words and substitute each of them with another word, which we randomly select according to its word2vec similarity to the substituted word. We concatenate the clean and noisy data and their phonetic encoded data. For each test sentence, we perform 0, 1, 2, 3, 4, or 5 times of the edit distance operation. We uniform randomly sample the word(s), the type of edit (from deletion, substitution, and insertion), and the substitution word. We trained models on below data: 1. Clean data (C); 2. Noisy data (N); 3. Noisy data concatenated with its Soundex encoding (N+S); 4. Combined clean and noisy data (C+N) as in [cheng2018towards].