Machine Translation is the task of translating between human languages using computers. Starting from simple word-for-word rule-based system in 1950s, we now have large multilingual neural models that can learn translate between dozens of languages. In our group we are particularly interested in MT for low-resource languages, in making MT systems robust to language variations (such as dialects or L2-speakers), in using MT for language documentation and education, and in direct speech translation.
- Fine-Tuning MT systems for Robustness to Second-Language Speaker Variations
- BembaSpeech: A Speech Recognition Corpus for the Bemba Language
- It's not a Non-Issue: Negation as a Source of Error in Machine Translation
- TICO-19: the Translation Initiative for COvid-19
- An Attentional Model for Speech Translation Without Transcription