Neural machine translation (NMT) suffers a performance deficiency when a limited vocabulary fails to cover the source or target side adequately, which happens frequently when dealing with morphologically rich languages. To address this problem, previous work focused on adjusting translation granularity or expanding the vocabulary size. However, morphological information is relatively under-considered in NMT architectures, which may further improve translation quality. We propose a novel method, which can not only reduce data sparsity but also model morphology through a simple but effective mechanism. By predicting the stem and suffix separately during decoding, our system achieves an improvement of up to 1.98 BLEU compared with previous wor...
This paper investigates how different features influence the translation quality of a Russian-Englis...
This paper presents the systems submitted by the University of Groningen to the English-Kazakh langu...
Various experiments from literature suggest that in statistical machine translation (SMT), applying ...
Translating into morphologically rich languages is a particularly difficult problem in machine trans...
A morphologically complex word (MCW) is a hierarchical constituent with meaning-preserving subunits,...
Translation into morphologically-rich languages challenges neural machine translation (NMT) models w...
We improve the quality of statistical machine translation (SMT) by applying models that predict word...
Recently, neural machine translation (NMT) has emerged as a powerful alternative to conventional st...
We propose a novel pipeline for translation into morphologically rich languages which consists of tw...
Lexical sparsity is a major challenge for machine translation into morphologically rich languages. W...
International audienceThis paper describes a two-step machine translation system that addresses the ...
The state of the art of handling rich morphology in neural machine translation (NMT) is to break wor...
Treating morphologically complex words (MCWs) as atomic units in translation would not yield a desi...
This paper presents an extension of neural machine translation (NMT) model to incorporate additional...
This thesis addresses some of the challenges of translating morphologically rich languages (MRLs). W...
This paper investigates how different features influence the translation quality of a Russian-Englis...
This paper presents the systems submitted by the University of Groningen to the English-Kazakh langu...
Various experiments from literature suggest that in statistical machine translation (SMT), applying ...
Translating into morphologically rich languages is a particularly difficult problem in machine trans...
A morphologically complex word (MCW) is a hierarchical constituent with meaning-preserving subunits,...
Translation into morphologically-rich languages challenges neural machine translation (NMT) models w...
We improve the quality of statistical machine translation (SMT) by applying models that predict word...
Recently, neural machine translation (NMT) has emerged as a powerful alternative to conventional st...
We propose a novel pipeline for translation into morphologically rich languages which consists of tw...
Lexical sparsity is a major challenge for machine translation into morphologically rich languages. W...
International audienceThis paper describes a two-step machine translation system that addresses the ...
The state of the art of handling rich morphology in neural machine translation (NMT) is to break wor...
Treating morphologically complex words (MCWs) as atomic units in translation would not yield a desi...
This paper presents an extension of neural machine translation (NMT) model to incorporate additional...
This thesis addresses some of the challenges of translating morphologically rich languages (MRLs). W...
This paper investigates how different features influence the translation quality of a Russian-Englis...
This paper presents the systems submitted by the University of Groningen to the English-Kazakh langu...
Various experiments from literature suggest that in statistical machine translation (SMT), applying ...