Improving Translations by Combining Fuzzy-Match Repair with Automatic Post-Editing

Two of the more predominant technologies that professional translators have at their disposal for improving productivity are machine translation (MT) and computer-aided translation (CAT) tools based on translation memories (TM). When translators use MT, they can use automatic post-editing (APE) systems to automate partof the post-editing work and get further productivity gains. When they use TM-based CAT tools, productivity may improve if they rely on fuzzy-match repair(FMR) methods. In this paper we combine FMR and APE: first a FMR proposal is produced from the translation unit proposed by the TM, then this proposal is further improved by an APE system specially tuned for this purpose. Experiments conducted on the translation of English texts into German show that, with the two combined technologies, the quality of the translations improves up to 23% compared to a pure MT system. The improvement over a pure FMR system is of 16%, showing the effectiveness of our joint solution

Towards a better integration of fuzzy matches in neural machine translation through data augmentation

We identify a number of aspects that can boost the performance of Neural Fuzzy Repair (NFR), an easy-to-implement method to integrate translation memory matches and neural machine translation (NMT). We explore various ways of maximising the added value of retrieved matches within the NFR paradigm for eight language combinations, using Transformer NMT systems. In particular, we test the impact of different fuzzy matching techniques, sub-word-level segmentation methods and alignment-based features on overall translation quality. Furthermore, we propose a fuzzy match combination technique that aims to maximise the coverage of source words. This is supplemented with an analysis of how translation quality is affected by input sentence length and fuzzy match score. The results show that applying a combination of the tested modifications leads to a significant increase in estimated translation quality over all baselines for all language combinations

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

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 ArabicEnglish 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 ArabicEnglish 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

Il post-editing: un'analisi della sua percezione e della compensazione equa

Nel seguente progetto di tesi viene trattata la compensazione equa del post-editing. In particolare, obiettivo della dissertazione è quello di comprendere quale sia la percezione del post-editing non solo tra i post-editor stessi, ma anche tra il pubblico generale, attraverso il metodo dell'associazione libera: sono state sottoposte due parole stimolo, "translation" e "post-editing", alle quali i partecipanti hanno associato dai 2 ai 5 nomi o aggettivi. È stato poi chiesto ai post-editor quale fosse, secondo la loro opinione, il metodo di remunerazione più equo per chi si occupa di tale pratica, al fine di comprendere quale venga maggiormente incontro alle esigenze dei post-editor e sia proporzionale allo sforzo richiesto. Inizialmente, viene inoltre proposta un'analisi della letteratura presente riguardo alla machine translation, la sua storia e diversi sistemi di traduzione automatica esistenti; successivamente, viene descritto dettagliatamente cosa si intende con post-editing, le sue caratteristiche e difficoltà, giungendo, infine, al tema chiave della tesi, ovvero i metodi di pagamento e la compensazione equa