User Adaptation in a Hybrid MT System: Feeding User Corrections into Synchronous Grammars and System Dictionaries

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Research areas: Year: 2012
Type of Publication: In Proceedings
Authors:
  • 47, 44 36
Volume: 7499
Book title: Proceedings of the Hybrid Machine Translation Workshop [held in conjunction with the 15th International Conference on Text, Speech and Dialogue [(TSD2012)]
Series: Lecture Notes in Artificial Intelligence Pages: 362-369
Address: Brno, Czech Republic
Organization: Hybrid Machine Translation Workshop [held in conjunction with the 15th International Conference on Text, Speech and Dialogue (TSD2012)] Month: September 3
Abstract:
In this paper we present the User Adaptation (UA) module implemented as part of a novel Hybrid MT translation system. The proposed UA module allows the user to enhance core system components such as synchronous grammars and system dictionaries at run-time. It is well-known that allowing users to modify system behavior raises the willingness to work with MT systems. However, in statistical MT systems user feedback is only 'a drop in the ocean' in the statistical resources. The hybrid MT system proposed here uses rule-based synchronous grammars that are automatically extracted out of small parallel annotated bilingual corpora. They account for structural mappings from source language to target language. Subsequent monolingual statistical components further disambiguate the target language structure. This approach provides a suitable substrate to incorporate a lightweight and effective UA module. User corrections are collected from a post-editing engine and added to the bilingual corpus, whereas the resulting additional structural mappings are provided to the system at run-time. Users can also enhance the system dictionary. User adaptation is organized in a user-specific commit-and-review cycle that allows the user to revise user adaptation input. Preliminary experimental evaluation shows promising results on the capability of the system to adapt to user structural preferences.
JRESEARCH_FULLTEXT: PreussEtAl_UA_TSD2102.pdf