This paper presents a systematic human evaluation of translations of English support verb constructions produced by a rule-based machine translation (RBMT) system (OpenLogos) and a statistical machine translation (SMT) system (Google Translate) for five languages: French, German, Italian, Portuguese and Spanish. We classify support verb constructions by means of their syntactic structure and semantic behavior and present a qualitative analysis of their translation errors. The study aims to verify how machine translation (MT) systems translate fine-grained linguistic phenomena, and how well-equipped they are to produce high-quality translation. Another goal of the linguistically motivated quality analysis of SVC raw output is to reinforce the need for better system hybridization, which leverages the strengths of RBMT to the benefit of SMT, especially in improving the translation of multiword units. Taking multiword units into account, we propose an effective method to achieve MT hybridization based on the integration of semantico-syntactic knowledge into SMT.

Logos 8, the next generation of the Logos Machine Translation (MT) system, is a client server application, which realizes the latest advances in system design and architecture. A multi-user, networkable application, Logos 8 allows Internet or Intranet use of its applications with client interfaces that communicate with dictionaries and translation servers through a common gateway. The new Logos 8 technology is based on a relational database for storage and organization of the lexical data. In this paper, we present Term-Builder, the Lexical Knowledge Acquisition tool developed for Logos 8. The new automatic coding functionality within Term-Builder is significantly improving the process of acquiring new lexicons for MT and other applications.