The development of Translation Technologies, like Translation Memory and Machine Translation, has completely changed the translation industry and translator's workflow in the last decades. Nevertheless, TM and MT have been developed separately until very recently. This ongoing project will study the external integration of TM and MT, examining if the productivity and post-editing efforts of translators are higher or lower than using only TM. To this end, we will conduct an experiment where translation students and professional translators will be asked to translate three short texts; then we will check the post-editing efforts (temporal, technical and cognitive efforts) and the quality of the translated texts.
In recent years, the emergence of streaming platforms such as Netflix, HBO or Amazon Prime Video has reshaped the field of entertainment [1], which increasingly relies on subtitling, dubbing or voice-over modes [2] [3]. However, little is known about audiovisual translation when dealing with Neural Machine Translation (NMT) engines. This work-in-progress paper seeks to examine the English subtitles of the first episode of the popular Spanish Netflix series Cable Girls and the translated version generated by Google Translate and DeepL. Such analysis will help us determine whether there are significant linguistic differences that could lead to miscomprehension or cultural shocks. To this end, the corpus compiled consists of the Spanish script, the English subtitles available on Netflix and the translated version of the script. As regards data analysis, errors have been classified following the DQF/MQM Error typology and have been evaluated with the automatic BLEU metric. Results show that NMT engines offer good-quality translations, which in turn may benefit translators working with audiovisual entertainment resources.
This study proposes an original methodology to underpin the operation of new generation Translation Memory (TM) systems where the translations to be retrieved from the TM database are matched not on the basis of Levenshtein (edit) distance but by employing innovative Natural Language Processing (NLP) and Deep Learning (DL) techniques. Three DL sentence encoders were experimented with to retrieve TM matches in English-Spanish sentence pairs from the DGT TM dataset. Each sentence encoder was compared with Okapi which uses edit distance to retrieve the best match.1 The automatic evaluation shows the benefit of the DL technology for TM matching and holds promise for the implementation of the TM tool itself, which is our next project.
El discurso académico ha despertado interés entre investigadores y profesores (Deroey, 2015; Mauranen, 2012; Hyland, 2010), en particular el uso de marcadores metadiscursivos. Sin embargo, se ha prestado poca atención a estas características apoyadas por la Traducción Automática (TA) en los contextos de AICLE. El objetivo del presente artículo es describir el uso y la frecuencia de los enfatizadores y atenuadores empleados en los ámbitos de la historia y la psicología y analizar la precisión de los equivalentes obtenidos en dos plataformas de TA, en concreto, DeepL y Google Translate. Para ello, se ha elaborado un pequeño corpus de dos seminarios y se han aplicado métodos cualitativos y cuantitativos para determinar la frecuencia y la precisión de los recursos lingüísticos bajo estudio. Los resultados han revelado que, si bien los elementos interaccionales proporcionados por la TA son precisos, pueden producirse omisiones y errores de traducción. Estas conclusiones pueden ser relevantes para los profesores de AICLE interesados en el discurso académico, así como para los investigadores de traducción que trabajan con corpus bilingües y multilingües y evalúan la exactitud de las herramientas de traducción.
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