Cognate-aware morphological segmentation for multilingual neural translation
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Date
2018-10-31
Department
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Language
en
Pages
8
390-397
390-397
Series
Third Conference on Machine Translation (WMT18); Brussels, Belgium
Abstract
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.Description
| openaire: EC/H2020/780069/EU//MeMAD
Keywords
neural machine translation, morphology, cognate, multilingual
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Citation
Grönroos, S-A, Virpioja, S & Kurimo, M 2018, Cognate-aware morphological segmentation for multilingual neural translation . in Third Conference on Machine Translation (WMT18); Brussels, Belgium . Association for Computational Linguistics, pp. 390-397, Conference on Machine Translation, Brussels, Belgium, 31/10/2018 . https://doi.org/10.18653/v1/W18-64037
