Cognate-aware morphological segmentation for multilingual neural translation

WS 2018 · Stig-Arne Grönroos, Sami Virpioja, Mikko Kurimo ·

This article describes the Aalto University entry to the WMT18 News Translation Shared Task. We participate in the multilingual subtrack with a system trained under the constrained condition to translate from English to both Finnish and Estonian. The system is based on the Transformer model. We focus on improving the consistency of morphological segmentation for words that are similar orthographically, semantically, and distributionally; such words include etymological cognates, loan words, and proper names. For this, we introduce Cognate Morfessor, a multilingual variant of the Morfessor method. We show that our approach improves the translation quality particularly for Estonian, which has less resources for training the translation model.

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Absolute Position Encodings • Adam • BPE • Dense Connections • Dropout • Label Smoothing • Layer Normalization • Linear Layer • Multi-Head Attention • Position-Wise Feed-Forward Layer • ReLU • Residual Connection • Scaled Dot-Product Attention • Softmax • Transformer

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Cognate-aware morphological segmentation for multilingual neural translation

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