A Machine-Aided Approach to Generating Grammar Rules from Japanese Source Text for Use in Hybrid and Rule-based Machine Translation Systems

Many automatic machine translation systems available today use a hybrid of pure statistical translation and rule-based grammatical translations. This is largely due to the shortcomings of each individual approach, requiring a large amount of time for linguistics experts to hand-code grammar rules for a rule-based system and requiring large amounts of source text to generate accurate statistical models. By automating a portion of the rule generation process, the creation of grammar rules could be made to be faster, more efficient and less costly. By doing statistical analysis on a bilingual corpus, common grammar rules can be inferred and exported to a hybrid system. The resulting rules then provide a base grammar for the system. This helps to reduce the time needed for experts to hand-code grammar rules and make a hybrid system more effective

Findings of the 2015 Workshop on Statistical Machine Translation

This paper presents the results of the WMT15 shared tasks, which included a standard news translation task, a metrics task, a tuning task, a task for run-time estimation of machine translation quality, and an automatic post-editing task. This year, 68 machine translation systems from 24 institutions were submitted to the ten translation directions in the standard translation task. An additional 7 anonymized systems were included, and were then evaluated both automatically and manually. The quality estimation task had three subtasks, with a total of 10 teams, submitting 34 entries. The pilot automatic postediting task had a total of 4 teams, submitting 7 entries