The Ubiqus E nglish- I nuktitut System for WMT 20 (2020)

Abstract: This paper describes Ubiqus’ submission to the WMT20 English-Inuktitut shared news translation task. Our main system, and only submission, is based on a multilingual approach, jointly training a Transformer model on several agglutinative languages. The English-Inuktitut translation task is challenging at every step, from data selection, preparation and tokenization to quality evaluation down the line. Difficulties emerge both because of the peculiarities of the Inuktitut language as well as the low-resource context.

## introduction ubiqus participated in the english to inuktitut news translation task of wmt20. we performed a single submission, based on an unconstrained multilingual setup. the approach consists of jointly training a traditional transformer @xcite model on several agglutinative languages in order to benefit from them for the low-resource english-inuktitut task @xcite . though the dataset provided for the task is sizable, with more than a million segments, it's quite narrow domain-wise, as it comes from proceedings of the nunavut hansard. the task being translation of news, it's expected to be a much wider domain. for that purpose, we extended the task with datasets of other -linguistically near -languages, as well as in-house datasets introducing more diversity to the domain. all experiments were performed with the opennmt @xcite toolkit, with tokenizer 1 for data preprocessing and opennmtpy 2 for model training and inference. ## future work our experiments remain in a rather traditional neural machine translation scope, with the only addition of multiple languages and dataset weighting. several paths can be explored from this starting point, such as adding more data for the current languages in the setup, authentic or synthetic (e.g. via back-translation @xcite ), or adding other languages that might share some common characteristics, like hungarian for instance. some additional work could also be explored on the tokenization part. for simplicity, our first approach in this paper relies on a very simple shared bpe approach. but, some more sophisticated approaches, maybe language-specific or morphologically adapted @xcite , may be worth exploring. finally, some more novel approaches could be tried, like massive pre-training methods such as bart @xcite . a similar experimental process could be followed, starting from only the core languages of the task (english and inuktitut), then extending to other languages and observe the impact. ## conclusion working on a new, unknown, language is always challenging. even more so when this language is quite distant from any language you're used to. also, automated metrics are far from being perfect for such tasks, especially in the context of such a particular language as inuktitut. particularly for this task, human evaluation is key. but, as data, it's quite a scarce resource for inuktitut. more knowledge of the language would be of tremendous help to better grasp the limits or interesting leads of the various models. one workaround can be to work on the opposite direction (inuktitut to english), but there is no guarantee the model would have similar behaviour for similar tricks. and, some knowledge about inuktitut would still be needed to analyze model behavior based on source inputs.