More here: Is machine translation a form of “augmentation”? An interview with Dr. Sharon O’Brien
One of the early panels on the first day was “Neural Machine Translation for Low-Resource Languages”, with speakers Jon Cambra and Eirini Zafeiridou. This basically focused on research into developing effective language models for languages that don’t have a substantial amount of data present on the internet.
According to Cambra and Zafeiridou, low-resource languages don’t necessarily mean smaller models. In fact, they will need more complex architectures in order to perform up to par with languages for which there is more data available.
Working with low resource languages is a research south korea mobile database concern that has become a lot more significant lately, especially with the recent news about Meta’s pioneering open-source No Language Left Behind Project (NLLB).
Learn more about the ramifications of the NLLB project on low resource languages in our article: No Language Left Behind: Meta’s Massive Multilingual Machine Translation Ambition Pays It Forward
The perfect machine translation system?
The first day capped off with a poster session that asked the question: “What if we had the perfect translation system?” One major highlight of this panel was the discussion on the role of the translator in such a scenario.
The translator’s skillset is a crucial factor in making the best use of such a machine translation system, going beyond translation and even beyond post-editing to take on a more evolved role.