I've read the documentation about the possibilities of using forced glossary and parallel corpus but I can't quite understand the difference. From the provided examples in the docs of Watson Language Translator I see that we use forced glossary when we want to emphasize on the translation of terms, phrases between different languages. However, with parallel corpus we provide whole translation of a sentence in different language.
So forced glossary is used for small amounts of data, or?
Forced glossary acts like a simple lookup, so that a word or phrase eg. StackOverflow doesn't get translated by the service. Instead a simple lookup and replacement action is performed. No machine learning is involved.
Parallel corpus is used as reinforced learning to get the service to understand how to translate niche or industry specific terminology and language. The translations are still performed by the machine learning service, it just has an improved understanding of how to perform the translations.