%0 Conference Proceedings %T Translating Similar Languages: Role of Mutual Intelligibility in Multilingual Transformers %A Adebara, Ife %A Nagoudi, El Moatez Billah %A Abdul Mageed, Muhammad %Y Barrault, Loïc %Y Bojar, Ondřej %Y Bougares, Fethi %Y Chatterjee, Rajen %Y Costa-jussà, Marta R. %Y Federmann, Christian %Y Fishel, Mark %Y Fraser, Alexander %Y Graham, Yvette %Y Guzman, Paco %Y Haddow, Barry %Y Huck, Matthias %Y Yepes, Antonio Jimeno %Y Koehn, Philipp %Y Martins, André %Y Morishita, Makoto %Y Monz, Christof %Y Nagata, Masaaki %Y Nakazawa, Toshiaki %Y Negri, Matteo %S Proceedings of the Fifth Conference on Machine Translation %D 2020 %8 November %I Association for Computational Linguistics %C Online %F adebara-etal-2020-translating %X In this work we investigate different approaches to translate between similar languages despite low resource limitations. This work is done as the participation of the UBC NLP research group in the WMT 2019 Similar Languages Translation Shared Task. We participated in all language pairs and performed various experiments. We used a transformer architecture for all the models and used back-translation for one of the language pairs. We explore both bilingual and multi-lingual approaches. We describe the pre-processing, training, translation and results for each model. We also investigate the role of mutual intelligibility in model performance. %U https://aclanthology.org/2020.wmt-1.42 %P 381-386