Training, Enhancing, Evaluating and Using MT Systems with Comparable Data

Babych, Bogdan; Chen, Yu; Eisele, Andreas; Hunsicker, Sabine; Pinnis, Mārcis; Skadiņa, Inguna; Skadiņš, Raivis; Thurmair, Gregor; Vasiļjevs, Andrejs; Verlic, Mateja; Zhang, Xiaojun

doi:10.1007/978-3-319-99004-0_6

Cited by 1 publication

(1 citation statement)

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“…After that, the nearest human assessment could be judged. A variety significant factors can provide a contribution to the bilingual evaluation understudy (BLUE) grossness [22]: i) synonyms and paraphrases will only be used if they are in a collection of various reference types [23]; ii) word results are similarly weighted so that there is no extra punishment for missing content-bearing content [24]; and iii) the punishment for brevity is a stop-gap measure to compensate for the relatively severe issue of not being prepared to calculate recall [25]. Each of these mistakes leads to an enhanced number of inappropriately indistinguishable transmissions in the assessment.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of domain sulfur industry for DIA translator using bilingual evaluation understudy method

Mohammed Lateef,

Muter Awaad,

Ali Hameed

et al. 2024

Bulletin EEI

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Evaluation is important part of our system development cycle; it also contributes to improving new machine translation (MT) technology optimum via comparing them with the traditional systems available to determine the weaknesses and the effectiveness to be improved in the proposed MT system. This work aiming to make a study that evaluate the performance and effectivness of the domain sulfur industry (DSI) for English-Arabic DIA translator quality. The recent study has conducted evaluating by making a comparison between this programme with the prominent Google translator through applying a rendering of 1,200 English sentences in bilingual evaluation understudy (BLUE) method. The obtain results show that the efficiency of Google translator is about 30.325%, while DIA translator efficiency in domain sulfur industry is about 73.325% and it’s more effective and give a better translation accuracy. The BLUE method efficiency is about (90.478%) compared with the human expert evaluator.

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Section: Methodsmentioning

confidence: 99%