Coursera Corpus Mining and Multistage Fine-Tuning for Improving Lectures Translation
Table 1: Number of sentence pairs in each corpus.
['Training schedule', 'BLEU']
However, given its small size, it can lead to only an unreliable MT system. to build a better MT system. We compared fine-tuning and mixed fine-tuning approaches proposed by \newciteraj17. When performing mixed fine-tuning on the concatenation of both two corpora, the TED corpus was oversampled to match the size of the ASPEC corpus. We trained our NMT models using tensor2tensor with its default hyper-parameters. So far, we do not have a test set for the target domain, i.e., Coursera. We therefore evaluated the performance of the MT systems with BLEU score The model first pre-trained and then mixed fine-tuned, i.e., A→AT, gave the best result on the TED test set. Thus, we used this model for sentence alignment.