Hybrid language processing in the Spoken Language Translator
<|reference_start|>Hybrid language processing in the Spoken Language Translator: The paper presents an overview of the Spoken Language Translator (SLT) system's hybrid language-processing architecture, focussing on the way in which rule-based and statistical methods are combined to achieve robust and efficient performance within a linguistically motivated framework. In general, we argue that rules are desirable in order to encode domain-independent linguistic constraints and achieve high-quality grammatical output, while corpus-derived statistics are needed if systems are to be efficient and robust; further, that hybrid architectures are superior from the point of view of portability to architectures which only make use of one type of information. We address the topics of ``multi-engine'' strategies for robust translation; robust bottom-up parsing using pruning and grammar specialization; rational development of linguistic rule-sets using balanced domain corpora; and efficient supervised training by interactive disambiguation. All work described is fully implemented in the current version of the SLT-2 system.<|reference_end|>
@article{rayner1997hybrid,
    title={Hybrid language processing in the Spoken Language Translator},
    author={Manny Rayner and David Carter (SRI International, Cambridge)},
    journal={arXiv preprint arXiv:cmp-lg/9701002},
    year={1997},
    number={CRC-064},
    archivePrefix={arXiv},
    eprint={cmp-lg/9701002},
    primaryClass={cmp-lg cs.CL}
}