Hybrid Machine Translation focuses on combining the best properties of different Machine Translation (MT) paradigms. Nowadays, it is very popular to include linguistic features in Statistical Machine Translation (SMT) systems or to modify the standard Rule-based Machine Translation (RBMT) architecture to include statistical knowledge. Other ways of hybridization include the system combination techniques which may combine a rich variety of translation paradigms.
This special issue in the prestigious Computer Speech and Language journal will cover different approaches from Hybrid Machine Translation and take advantage of the latest and leading research to discuss the progress in the field.
This special issue invites contributions related to (but not limited):
- theoretical and experimental design of hybrid MT architectures
- experimental results with hybrid MT systems guided by corpus-based or rule-based systems
- introduction of linguistics in corpus-based approaches
- rule-based systems extended or built with statistical information
- induction of lexical or grammatical transfer rules from corpora
- description of open source tools and language resources for hybrid MT
- description of computationally efficient algorithms for hybrid MT
- applications of hybrid MT systems
- hybrid methods applied to spoken language translation (SLT)
- hybrid evaluation methods
- system combination of different MT and SLT paradigms.
Prospective authors should follow the regular guidelines of the Computer Speech and Language Journal for electronic submission. During submission authors must select "SI: Hybrid Machine Translation" as Article Type.
Tentative Schedule: 1st June 2014: Notification of resubmission 1st November 2014: Deadline for Camera Ready versions