Example retrieval from a translation memory

Cranias, Lambros; Papageorgiou, Harris; Piperidis, Stelios

doi:10.1017/s1351324997001447

Cited by 15 publications

(5 citation statements)

References 18 publications

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“…One obvious way in which we could enhance this implementation would be to use an N-gram index as proposed by Nagao and Mori (1994). Dynamic Programming (DP) techniques would undoubtedly lead to greater efficiency, as suggested by Cranias et al (1995Cranias et al ( , 1997 and also Planas and Furuse (this volume).…”

Section: Retrieval Speed Optimisationmentioning

confidence: 99%

The effects of word order and segmentation on translation retrieval performance

Baldwin

Hozumi

2000

Proceedings of the 18th Conference on Computational Linguistics -

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This research looks at the effects of word order and segmentation on translation retrieval performance for an experimental Japanese-English translation memory system. We implement a number of both bag-of-words and word order-sensitive similarity metrics, and test each over characterbased and word-based indexing. The translation retrieval performance of each system configuration is evaluated empirically through the notion of word edit distance between translation candidate outputs and the model translation. Our results indicate that character-based indexing is consistently superior to word-based indexing, suggesting that segmentation is an unnecessary luxury in the given domain. Word order-sensitive approaches are demonstrated to generally outperform bag-of-words methods, with source language segment-level edit distance proving the most effective similarity metric.

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Section: Retrieval Speed Optimisationmentioning

confidence: 99%

The effects of word order and segmentation on translation retrieval performance

Baldwin

Hozumi

2000

Proceedings of the 18th Conference on Computational Linguistics -

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“…A first evaluation of the MSSM speed gives 0.5 to 2 milliseconds for comparing only s two randomly chosen English or Japanese sentences over 3 levels (word, lemmas, POS). The 4 Cranias et al (1997), Thompson & Brew (1994, or in a more specific way, Lcpage (1998) 5 Without the shallow analysis implementation has been done with a DELL Optiplex GX 1 233 Mhz, Window NT, Java 1 18. This algorithm can be improved in different ways.…”

Section: Discussionmentioning

confidence: 99%

Multi-level similar segment matching algorithm for translation memories and Example-based Machine Translation

Planas¹,

Furuse²

2000

Proceedings of the 18th Conference on Computational Linguistics -

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We propose a dynamic programming algorithm for calculaing the similarity between two segmeuts of words of the same language. The similarity is considered as a vector whose coordinates refer to the levels of analysis of the segments. This algorithm is extremely efficient for retrieving the best example in Translation Memory systems. The calculus being constructive, it also gives the correspondences between the words of the two segments. This allows the extension of Translation Memory systems towards Example-based Machiue Translation.

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“…The string-edit distance algorithm is widely used, sometimes effectively weighting certain words or categories favourably (e.g. Cranias et al, 1997;Furuse & Iida, 1994;Veale & Way, 1997).…”

Section: Representation and Retrievalmentioning

confidence: 99%

Example-Based Machine Translation

Brown¹

2017

Machine Translation

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Previous work has shown that grammars and similar structure can be induced from unlabeled text (both monolingually and bilingually), and that the performance of an example-based machine translation (EBMT) system can be substantially enhanced by using clustering techniques to determine equivalence classes of individual words which can be used interchangeably, t h us converting translation examples into templates. This paper describes the combination of these two a pproaches to further increase the coverage (or conversely, decrease the required training text) of an EBMT system. Preliminary results show t h a t a reduction in required training text by a factor of twelve is possible for translation from French i n to English. References ARTFL Project. 1998. ARTFL Project: French-English Dictionary. Project for American and French Research on the Treasury of the French Language, University of Chicago. http://humanities.uchicago.edu/ARTFL.html.

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Example retrieval from a translation memory

Cited by 15 publications

References 18 publications

The effects of word order and segmentation on translation retrieval performance

The effects of word order and segmentation on translation retrieval performance

Multi-level similar segment matching algorithm for translation memories and Example-based Machine Translation

Example-Based Machine Translation

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