<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In this article we present a novel linguistically driven evaluation method and apply it to the main approaches of Machine Translation (Rule-based, Phrase-based, Neural) to gain insights into their strengths and weaknesses in much more detail than provided by current evaluation schemes. Translating between two languages requires substantial modelling of knowledge about the two languages, about translation, and about the world. Using English-German IT-domain translation as a case-study, we also enhance the Phrase-based system by exploiting parallel treebanks for syntax-aware phrase extraction and by interfacing with Linked Open Data (LOD) for extracting named entity translations in a post decoding framework.</p></abstract>

In this article we present a novel linguistically driven evaluation method and apply it to the main approaches of Machine Translation (Rule-based, Phrase-based, Neural) to gain insights into their strengths and weaknesses in much more detail than provided by current evaluation schemes. Translating between two languages requires substantial modelling of knowledge about the two languages, about translation, and about the world. Using English-German IT-domain translation as a case-study, we also enhance the Phrase-based system by exploiting parallel treebanks for syntax-aware phrase extraction and by interfacing with Linked Open Data (LOD) for extracting named entity translations in a post decoding framework.

Machine Translation: Phrase-Based, Rule-Based and Neural Approaches with Linguistic Evaluation

German Research Center for Artificial Intelligence (DFKI)

Institute of Formal and Applied Linguistics

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

In this article we present a novel linguistically driven evaluation method and apply it to the main approaches of Machine Translation (Rule-based,...

Machine Translation: Phrase-Based, Rule-Based and Neural Approaches with Linguistic Evaluation

Publié en ligne: 26 juin 2017

Pages: 28 - 43

DOI: https://doi.org/10.1515/cait-2017-0014

Mots clés
Machine translation, Parallel treebanks, Entity linking, Manual evaluation, Neural approaches

© 2017 Vivien Macketanz et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Machine Translation: Phrase-Based, Rule-Based and Neural Approaches with Linguistic Evaluation

Publié en ligne: 26 juin 2017

Pages: 28 - 43

DOI: https://doi.org/10.1515/cait-2017-0014

Mots clésMachine translation, Parallel treebanks, Entity linking, Manual evaluation, Neural approaches

© 2017 Vivien Macketanz et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
Machine translation, Parallel treebanks, Entity linking, Manual evaluation, Neural approaches