Abstract
This paper describes the joint submission of the University of Edinburgh and Uppsala University to the WMT’20 chat translation task for both language directions (English↔German). We use existing state-of-the-art machine translation models trained on news data and fine-tune them on in-domain and pseudo-indomain web crawled data. We also experiment with (i) adaptation using speaker and domain tags and (ii) using different types and amounts
of preceding context. We observe that contrarily to expectations, exploiting context degrades the results (and on analysis the data is not highly contextual). However using domain tags does improve scores according to the automatic evaluation. Our final primary systems use domain tags and are ensembles of
4 models, with noisy channel reranking of outputs. Our en-de system was ranked second in the shared task while our de-en system outperformed all the other system
of preceding context. We observe that contrarily to expectations, exploiting context degrades the results (and on analysis the data is not highly contextual). However using domain tags does improve scores according to the automatic evaluation. Our final primary systems use domain tags and are ensembles of
4 models, with noisy channel reranking of outputs. Our en-de system was ranked second in the shared task while our de-en system outperformed all the other system
| Originalsprog | Engelsk |
|---|---|
| Titel | 5th Conference on Machine Translation, WMT 2020 - Proceedings |
| Antal sider | 6 |
| Publikationsdato | 2020 |
| Sider | 473-478 |
| ISBN (Trykt) | 9781948087810 |
| Status | Udgivet - 2020 |
| Udgivet eksternt | Ja |