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The paper describes Tilde's EnglishLatvian and Latvian-English machine translation systems for the WMT 2017 shared task in news translation. Both constrained and unconstrained systems are described. Our constrained systems were ranked as the best performing systems according to the automatic evaluation results. The paper gives details to how we pre-processed training data, the NMT system architecture that we used for training the NMT models, the SMT systems and their usage in NMT-SMT hybrid system configurations.

Intent detection is one of the main tasks of a dialogue system. In this paper, we present our intent detection system that is based on fastText word embeddings and a neural network classifier. We find an improvement in fastText sentence vectorization, which, in some cases, shows a significant increase in intent detection accuracy. We evaluate the system on languages commonly spoken in Baltic countries—Estonian, Latvian, Lithuanian, English, and Russian. The results show that our intent detection system provides state-of-the-art results on three previously published datasets, outperforming many popular services. In addition to this, for Latvian, we explore how the accuracy of intent detection is affected if we normalize the text in advance.

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