| openaire: EC/H2020/780069/EU//MeMADThis 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 resource...
A morphologically complex word (MCW) is a hierarchical constituent with meaning-preserving subunits,...
Various experiments from literature suggest that in statistical machine translation (SMT), applying ...
Cross-lingual word embedding models learn a shared vector space for two or more lan- guages so that ...
This paper describes the University of Helsinki Language Technology group's participation in the WMT...
VK: COINThis article describes the Aalto University entry to the English-to-Finnish news translation...
Abstract We propose a language-independent approach for improving statistical machine translation fo...
| openaire: EC/H2020/771113/EU//FoTranIn our submission to the SIGMORPHON 2022 Shared Task on Morphe...
This thesis addresses some of the challenges of translating morphologically rich languages (MRLs). W...
The state of the art of handling rich morphology in neural machine translation (NMT) is to break wor...
Translation into morphologically-rich languages challenges neural machine translation (NMT) models w...
The use of morphology is particularly interesting in the context of statistical machine translation ...
This paper presents the systems submitted by the University of Groningen to the English-Kazakh langu...
Neural machine translation (NMT) suffers a performance deficiency when a limited vocabulary fails to...
While the intuition that morphological preprocessing of languages in various applications can be ben...
Translating to and from low-resource polysynthetic languages present numerous challenges for NMT. We...
A morphologically complex word (MCW) is a hierarchical constituent with meaning-preserving subunits,...
Various experiments from literature suggest that in statistical machine translation (SMT), applying ...
Cross-lingual word embedding models learn a shared vector space for two or more lan- guages so that ...
This paper describes the University of Helsinki Language Technology group's participation in the WMT...
VK: COINThis article describes the Aalto University entry to the English-to-Finnish news translation...
Abstract We propose a language-independent approach for improving statistical machine translation fo...
| openaire: EC/H2020/771113/EU//FoTranIn our submission to the SIGMORPHON 2022 Shared Task on Morphe...
This thesis addresses some of the challenges of translating morphologically rich languages (MRLs). W...
The state of the art of handling rich morphology in neural machine translation (NMT) is to break wor...
Translation into morphologically-rich languages challenges neural machine translation (NMT) models w...
The use of morphology is particularly interesting in the context of statistical machine translation ...
This paper presents the systems submitted by the University of Groningen to the English-Kazakh langu...
Neural machine translation (NMT) suffers a performance deficiency when a limited vocabulary fails to...
While the intuition that morphological preprocessing of languages in various applications can be ben...
Translating to and from low-resource polysynthetic languages present numerous challenges for NMT. We...
A morphologically complex word (MCW) is a hierarchical constituent with meaning-preserving subunits,...
Various experiments from literature suggest that in statistical machine translation (SMT), applying ...
Cross-lingual word embedding models learn a shared vector space for two or more lan- guages so that ...