After coming up with some custom rules for my language of study (German), I got a taste of the potential for these powerful rules. In addition to learning to train an SMT engine, we also received an arguably indispensable tool for localization: Regular Expressions (Regex). You can find our presentation and proposal in the link below. That being said, there were noticeable time and cost savings that we calculated over human translation for our engine, but the MT quality was far too underwhelming to justify further training, especially considering the history of our BLEU score (which peaked on the second of ten models). Compared to our initial model’s MT output, there wasn’t a significant change. To use them for our engine, we aligned them using the online CAT tool Memsource.Īfter training the engine, we performed a final evaluation of the quality of MT output. The “testing” files would be used to sample the quality of the engine’s output after the training had concluded. For those, we used ocean science-centric speeches. The “tuning” files, then, would be closer in domain to our desired results. These gave our engine a basis with which to build its translation rules from. The “testing” files were general subject speeches pulled from a corpus of TEDTalks speeches from 2013. This score is currently the industry standard, but has several disadvantages, which I won’t get into here for the sake of brevity, but, suffice it to say, for the purposes of our project, increases in the BLEU score meant positive results for our engine.
TOPTRACKER CHANGE PROJECT SERIES
To accomplish this, we compiled a series of “training”, “tuning”, and “testing” bitext files in an attempt to improve the BLEU score of the engine. Our goal was to train an engine to effectively machine translate TEDTalks speeches covering marine biology and oceanography. And so, as part of our up-to-date curriculum at MIIS, we engaged in a small project whereby we trained an SMT engine using Microsoft Custom Translator, ran an evaluation on our results, and drafted a proposal to a “client” with our recommendations for potential further training.
Although their prevalence is somewhat controversial, they are nevertheless an important consideration for any stakeholders. Statistical Machine Translation (SMT) and, even more so, Neural Machine Translation (NMT) seem to be hot topics in the language industries. Let’s first focus on translation engine training for a moment. In this post, I’d like to walk you through a few of the projects I completed during the Spring semester of 2019.
TOPTRACKER CHANGE PROJECT HOW TO
However, during my stay at the Middlebury Institute of International Studies (MIIS), I had gotten some exposure to translation engine training software, various CAT tools, as well as how to utilize Regular Expressions to streamline the localization process. With the rapidly growing diversity of CAT tools in this era of Machine Translation (MT) and AI development, keeping up with the latest trends can be a daunting task.
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