Search Results for author: Inguna Skadi{\c{n}}a

Found 10 papers, 0 papers with code

The Competitiveness Analysis of the European Language Technology Market

no code implementations LREC 2020 Andrejs Vasi{\c{l}}jevs, Inguna Skadi{\c{n}}a, Indra Samite, Kaspars Kauli{\c{n}}{\v{s}}, {\=E}riks Ajausks, J{\=u}lija Me{\c{l}}{\c{n}}ika, Aivars B{\=e}rzi{\c{n}}{\v{s}}

This paper presents the key results of a study on the global competitiveness of the European Language Technology market for three areas {--} Machine Translation, speech technology, and cross-lingual search.

Machine Translation Translation

NMT or SMT: Case Study of a Narrow-domain English-Latvian Post-editing Project

no code implementations IJCNLP 2017 Inguna Skadi{\c{n}}a, M{\=a}rcis Pinnis

The recent technological shift in machine translation from statistical machine translation (SMT) to neural machine translation (NMT) raises the question of the strengths and weaknesses of NMT.

Machine Translation Translation

Syntax-based Multi-system Machine Translation

no code implementations LREC 2016 Mat{\=\i}ss Rikters, Inguna Skadi{\c{n}}a

This paper describes a hybrid machine translation system that explores a parser to acquire syntactic chunks of a source sentence, translates the chunks with multiple online machine translation (MT) system application program interfaces (APIs) and creates output by combining translated chunks to obtain the best possible translation.

Language Modelling Machine Translation +1

CLARA: A New Generation of Researchers in Common Language Resources and Their Applications

no code implementations LREC 2014 Koenraad De Smedt, Erhard Hinrichs, Detmar Meurers, Inguna Skadi{\c{n}}a, Bolette Pedersen, Costanza Navarretta, N{\'u}ria Bel, Krister Lind{\'e}n, Mark{\'e}ta Lopatkov{\'a}, Jan Haji{\v{c}}, Gisle Andersen, Przemyslaw Lenkiewicz

CLARA (Common Language Resources and Their Applications) is a Marie Curie Initial Training Network which ran from 2009 until 2014 with the aim of providing researcher training in crucial areas related to language resources and infrastructure.

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