Type

Journal Article

Authors

Andy Way
Qun Liu
Peyman Passban

Subjects

Linguistics

Topics
smt german statistical machine translation modeling language neural networks machine translating component surface

Providing morphological information for SMT using neural networks (2017)

Abstract Treating morphologically complex words (MCWs) as atomic units in translation would not yield a desirable result. Such words are complicated constituents with meaningful subunits. A complex word in a morphologically rich language (MRL) could be associated with a number of words or even a full sentence in a simpler language, which means the surface form of complex words should be accompanied with auxiliary morphological information in order to provide a precise translation and a better alignment. In this paper we follow this idea and propose two different methods to convey such information for statistical machine translation (SMT) models. In the first model we enrich factored SMT engines by introducing a new morphological factor which relies on subword-aware word embeddings. In the second model we focus on the language-modeling component. We explore a subword-level neural language model (NLM) to capture sequence-, word- and subword-level dependencies. Our NLM is able to approximate better scores for conditional word probabilities, so the decoder generates more fluent translations. We studied two languages Farsi and German in our experiments and observed significant improvements for both of them.
Collections Ireland -> Dublin City University -> DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing: School of Computing
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres: ADAPT
Ireland -> Dublin City University -> Publication Type = Article
Ireland -> Dublin City University -> Status = Published
Ireland -> Dublin City University -> Subject = Computer Science: Machine translating

Full list of authors on original publication

Andy Way, Qun Liu, Peyman Passban

Experts in our system

1
Andy Way
Dublin City University
Total Publications: 229
 
2
Qun Liu
Dublin City University
Total Publications: 31
 
3
Peyman Passban
Dublin City University
Total Publications: 9