Bootstrapping a Hybrid MT System to a New Language Pair
The usual concern when opting for a rule-based or a hybrid machine translation (MT) system is how much effort is required to adapt the system to a different language pair or a new domain. In this paper, we describe a way of adapting an existing hybrid MT system to a new language pair, and show that such a system can outperform a standard phrase-based statistical machine translation system with an average of 10 persons/month of work. This is specifically important in the case of domain-specific MT for which there is not enough parallel data for training a statistical machine translation system.
Code (0)
등록된 구현이 없습니다.
Tasks
Machine TranslationTranslationSimilar Papers 제목 키워드 기반
Bootstrapping a Natural Language Interface to a Cyber Security Event Collection System using a Hybrid Translation Approach
Dr. Boot: Bootstrapping Program Synthesis Language Models to Perform Repairing
Language models for program synthesis are usually trained and evaluated on programming competition datasets (MBPP, APPS). However, these datasets are limited in size and quality, while these language models are extremely…
Reinforcement LearningProgram SynthesisBootstrapping a hybrid deep MT system
Bootstrapping Unsupervised Bilingual Lexicon Induction
The task of unsupervised lexicon induction is to find translation pairs across monolingual corpora. We develop a novel method that creates seed lexicons by identifying cognates in the vocabularies of related languages on…
Bilingual Lexicon InductionSemantic Textual SimilarityTranslation