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

Cybernetics and Information Technologies

Avramidis Eleftherios¹  Srivastava Ankit¹  Burchardt Aljoscha¹  Macketanz Vivien¹  Helcl Jindrich² 

[1] German Research Center for Artificial Intelligence (DFKI), Language Technology Lab, Berlin, Germany;Institute of Formal and Applied Linguistics, Charles University, Czech Republic;

关键词: machine translation;    parallel treebanks;    entity linking;    manual evaluation;    neural approaches;   

DOI  :  10.1515/cait-2017-0014

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown