Towards Optimal Choice Selection for Improved Hybrid Machine Translation

Towards Optimal Choice Selection for Improved Hybrid Machine Translation

In recent years, machine translation (MT) research focused on investigating how hybridMT as well as MT combination systems can be designed so that the resulting translations give an improvement over the individual translations. As a first step towards achieving this objective we have developed a parallel corpus with source data and the output of a number of MT systems, annotated with metadata i...

Towards Hybrid Quality-Oriented Machine Translation

We present a hybrid MT architecture, combining state-of-the-art linguistic processing with advanced stochastic techniques. Grounded in a theoretical reflection on the division of labor between rule-based and probabilistic elements in the MT task, we summarize per-component approaches to ranking, including empirical results when evaluated in isolation. Combining component-internal scores and a n...

Machine Learning for Hybrid Machine Translation

We describe a substitution-based system for hybrid machine translation (MT) that has been extended with machine learning components controlling its phrase selection. The approach is based on a rule-based MT (RBMT) system which creates template translations. Based on the rule-based generation parse tree and target-to-target alignments, we identify the set of “interesting” translation candidates ...

Lexical selection for machine translation

Towards Semantic-based Hybrid Machine Translation between Bulgarian and English

The paper focuses on the creation of a semantic-based hybrid Machine Translation system between Bulgarian and English in the domain of Information Technology. The preprocessing strategies are presented. A method for the substitution of English word forms with the synsets or Bulgarian representative lemmas is discussed. Finally, the creation of a factored model in the Moses system is described.