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        <title>Applicability of Resource-based Machine Translation to Airplane Manuals</title>
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            <title>Proceedings of the 8th Conference of the Association for Machine Translation in the Americas: Government and Commercial Uses of MT</title>
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    <abstract>Machine translation (MT) has been studied and developed since the advent of computers, and yet is rarely used in actual business. For business use, rule-based MT has been developed, but it requires rules and a domain-specific dictionary that have been created manually. On the other hand, as huge amounts of text data have become available, corpus-based MT has been actively studied, particularly corpus-based statistical machine translation (SMT). In this study, we tested and verified the usefulness of SMT for aviation manuals. Manuals tend to be similar and repetitive, so SMT is powerful even with a small amount of training data. Although our experiments with SMT are at the preliminary stage, the BLEU score is high. SMT appears to be a powerful and promising technique in this domain.</abstract>
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%0 Conference Proceedings
%T Applicability of Resource-based Machine Translation to Airplane Manuals
%A Yamamoto, Eiko
%A Terada, Akira
%A Isahara, Hitoshi
%S Proceedings of the 8th Conference of the Association for Machine Translation in the Americas: Government and Commercial Uses of MT
%D 2008
%8 oct 21 25
%I Association for Machine Translation in the Americas
%C Waikiki, USA
%F yamamoto-etal-2008-applicability
%X Machine translation (MT) has been studied and developed since the advent of computers, and yet is rarely used in actual business. For business use, rule-based MT has been developed, but it requires rules and a domain-specific dictionary that have been created manually. On the other hand, as huge amounts of text data have become available, corpus-based MT has been actively studied, particularly corpus-based statistical machine translation (SMT). In this study, we tested and verified the usefulness of SMT for aviation manuals. Manuals tend to be similar and repetitive, so SMT is powerful even with a small amount of training data. Although our experiments with SMT are at the preliminary stage, the BLEU score is high. SMT appears to be a powerful and promising technique in this domain.
%U https://aclanthology.org/2008.amta-govandcom.25
%P 464-469

Markdown (Informal)

[Applicability of Resource-based Machine Translation to Airplane Manuals](https://aclanthology.org/2008.amta-govandcom.25) (Yamamoto et al., AMTA 2008)

• Applicability of Resource-based Machine Translation to Airplane Manuals (Yamamoto et al., AMTA 2008)

ACL

• Eiko Yamamoto, Akira Terada, and Hitoshi Isahara. 2008. Applicability of Resource-based Machine Translation to Airplane Manuals. In Proceedings of the 8th Conference of the Association for Machine Translation in the Americas: Government and Commercial Uses of MT, pages 464–469, Waikiki, USA. Association for Machine Translation in the Americas.