# Cognate-aware morphological segmentation for multilingual neural translation

arXiv: https://arxiv.org/abs/1808.10791 · HTML: https://mlsanity.com/paper/1808.10791/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2018.

## Built on by (most cited first, 1 of 1)

- [The Effectiveness of Morphology-aware Segmentation in Low-Resource Neural Machine Translation](https://mlsanity.com/paper/2103.11189/) (2021)

## Abstract (arXiv)

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.