Interlingua-based translation for language learning systems
John Lee, Stephanie Seneff · 2005
This paper concerns our recent research in developing high-quality spoken language translation for restricted domains. The intended application is a spoken-language translation aid for a student of a foreign language. A significant novelty of the work is in leveraging an existing English-to-Mandarin translation system in the weather domain both to provide a corpus of sentence pairs for training and to induce an initial version of the parsing grammar for translation in the reverse direction. Using an interlingual approach, we are able to reject strings that fail to parse, yielding high accuracy on any translations provided to the student. On a test set of 369 naturally spoken Mandarin queries, the translation was judged incorrect for fewer than 3% of the query transcripts. A statistical phrase-based translation system performed significantly worse, when trained on the same material