Coping with Zero-Shot Translation and its Explainability

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In the last couple of years, the rise of Artificial Intelligence has been remarkable. The data-driven Artificial Intelligence techniques are behind so many of the recent successes and the effects they have on our society. In the field of Natural Language Processing (NLP), a sub-field of Artificial Intelligence, which has close ties with Machine Learning, Neural Machine Translation (NMT) has achieved state-of-the-art translation quality in various research evaluations campaigns when using large amounts of training data. However, such parallel corpora are not widely available for all language pairs and domains. Thus, there is a lot of room for improvement regarding the quality of translation, processing very low-resourced languages and language variants, considering polysynthetic languages, handling the out-of vocabulary, using multiple modalities, translating multiple sentences, , etc. The long-term objective of my research program will deal with the scarcity of algorithm methods for building efficient and transparent zero-shot translation systems and will handle original and challenging problems that give rise to novel algorithms and techniques in the field of machine translation applied to North American indigenous languages.  We intend to contribute to the important challenge of unsupervised machine translation in zero-shot resource settings and an explainable environment, while creating new translation models relying on nothing but monolingual resources and knowledge representations related to this pair of languages. Moreover, we intend to develop new algorithms that explain intermediate outcomes and provide reasoning for the proposed solutions of the automatic translation system, thus unpacking the black box while maintaining a high level of learning performance. The proposed  research Discovery  program will lead to revolutionary advances in the area of machine translation and language resources development and will contribute to the development of innovative technological tools applied to indigenous languages. Moreover, this research program will allow me to consolidate and expand my industrial and international collaborations and will create an excellent research environment for training Highly Qualified Personals in the area of Natural Language Processing and Artificial Intelligence, within three graduate programs in computer science at UQAM.  This research program will consider all forms of diversity to lead to higher-impact research, fosters greater innovation, and results in enhanced performance. As this proposed program will focus on North American indigenous languages as polysynthetic languages, this will help attract talents in indigenous communities and from different ethnic or cultural backgrounds, to strengthen university research, to improve the quality of life for indigenous people and to contribute to stronger economic growth in indigenous communities, and in Canada as a whole.