AL-TERp: Extended Metric for Machine Translation Evaluation of Arabic

Marouani, Mohamed El; Boudaa, Tarik; Enneya, Nourddine

doi:10.1007/978-3-319-59569-6_17

Cited by 4 publications

(1 citation statement)

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“…Although METEOR introduced recall in the calculation of its score in order to overcome one of BLEU's drawbacks, not all of its features support Arabic. The specific difficulty when translating to and from Arabic is the lack of resources available for linguistic analysis of, for example, word stems, with Porter stemmer, or synonyms, with the standard Wordnet (El Marouani et al 2017).…”

Section: Automatic Evaluation Developmentmentioning

confidence: 99%

Comparative Evaluation of Translation Memory (TM) and Machine Translation (MT) Systems in Translation between Arabic and English

Khaled¹

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In general, advances in translation technology tools have enhanced translation quality significantly. Unfortunately, however, it seems that this is not the case for all language pairs. A concern arises when the users of translation tools want to work between different language families such as Arabic and English. The main problems facing Arabic<>English translation tools lie in Arabic’s characteristic free word order, richness of word inflection – including orthographic ambiguity – and optionality of diacritics, in addition to a lack of data resources. The aim of this study is to compare the performance of translation memory (TM) and machine translation (MT) systems in translating between Arabic and English.The research evaluates the two systems based on specific criteria relating to needs and expected results. The first part of the thesis evaluates the performance of a set of well-known TM systems when retrieving a segment of text that includes an Arabic linguistic feature. As it is widely known that TM matching metrics are based solely on the use of edit distance string measurements, it was expected that the aforementioned issues would lead to a low match percentage. The second part of the thesis evaluates multiple MT systems that use the mainstream neural machine translation (NMT) approach to translation quality. Due to a lack of training data resources and its rich morphology, it was anticipated that Arabic features would reduce the translation quality of this corpus-based approach. The systems’ output was evaluated using both automatic evaluation metrics including BLEU and hLEPOR, and TAUS human quality ranking criteria for adequacy and fluency.The study employed a black-box testing methodology to experimentally examine the TM systems through a test suite instrument and also to translate Arabic English sentences to collect the MT systems’ output. A translation threshold was used to evaluate the fuzzy matches of TM systems, while an online survey was used to collect participants’ responses to the quality of MT system’s output. The experiments’ input of both systems was extracted from Arabic<>English corpora, which was examined by means of quantitative data analysis. The results show that, when retrieving translations, the current TM matching metrics are unable to recognise Arabic features and score them appropriately. In terms of automatic translation, MT produced good results for adequacy, especially when translating from Arabic to English, but the systems’ output appeared to need post-editing for fluency. Moreover, when retrievingfrom Arabic, it was found that short sentences were handled much better by MT than by TM. The findings may be given as recommendations to software developers.

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Section: Automatic Evaluation Developmentmentioning

confidence: 99%