Adaptation using out-of-domain corpus within EBMT

Doi, Takao; Sumita, Eiichiro; Yamamoto, Hirotsugu

doi:10.3115/1073483.1073489

Cited by 1 publication

(1 citation statement)

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“…In Brown et al (1993) and Callison-Burch et al (2004), large in-domain monolingual and bilingual corpora are used to train a language model for each of the languages involved and a translation model for each pair of them. Since it is difficult to find bilingual corpora, other techniques, such as Doi et al (2003), can be employed to perform the training: a small-sized bilingual corpus is first analysed, then the effectiveness of the translation model obtained is boosted by using adaptation techniques with out-of-domain monolingual corpora. While all these techniques avoid the problem of evaluating the goodness (similarity) of the translations, they are even more dependent on the particular language and subject that they were trained for.…”

Section: Statistical Approachesmentioning

confidence: 99%

EXTRA: a system for example-based translation assistance

Mandreoli

Tiberio

2007

Machine Translation

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In this paper we present EXTRA (EXample-based TRanslation Assistant), a translation memory (TM) system. EXTRA is able to propose effective translation suggestions by relying on syntactic analysis of the text and on a rigorous, language-independent measure; the search is performed efficiently in large amounts of bilingual texts thanks to its advanced retrieval techniques. EXTRA does not use external knowledge requiring the intervention of users and is completely customizable and portable as it has been implemented on top of a standard DataBase Management System. The paper provides a thorough evaluation of both the effectiveness and the efficiency of our system. In particular, in order to quantify the benefits offered by EXTRA assisted translation over manual translation, we introduce a simulator implementing specifically devised statistical, process-oriented, discrete-event models. As far as we know, this is the first time statistical simulation experiments have been used to face the nontrivial problem of evaluating TM systems, particularly for comparing the time that could be saved by performing assisted translation versus "manual" translation and for optimally tuning the system behaviour with respect to differently skilled users. In our experiments, we considered three scenarios, manual translation with one or two translators and assisted translation with one translator. The time needed for one translator to do an assisted translation is significantly closer to that of a team of two translators than to that of the single translator. The mean sentence translation time is by far the lowest for this scenario, corresponding to the highest per translator productivity. 123 168 F. Mandreoli et al.We also estimate the total translation time when the number of query sentences, the maximum number of suggestions to be read, and the probability of look up are varied: the best trade-off is given by reading (and presenting) four or five suggestions at the most.

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Section: Statistical Approachesmentioning

confidence: 99%