Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userAudiovisual Translation through NMT and Subtitling in the Netflix Series `Cable Girls'
الترجمة السمعية البصرية من خلال NMT وترجمة في سلسلة Netflix `الفتيات الكابلات
410
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
في السنوات الأخيرة، أعاد ظهور منصات تدفق مثل Netflix أو HBO أو Amazon Prime Prieet في مجال الترفيه، والذي يعتمد بشكل متزايد على أوضاع الروائح أو الصوت على الصوت. ومع ذلك، يعرف القليل عن الترجمة السمعية البصرية عند التعامل مع محركات الترجمة الآلية العصبية (NMT). تسعى ورقة العمل في العمل هذه إلى فحص الترجمات الإنجليزية من الحلقة الأولى للفتيات الكابلات الإسبانية سلسلة Netflix الشهيرة والنسخة المترجمة التي تم إنشاؤها بواسطة Google Translate و Deepl. سيساعدنا هذا التحليل في تحديد ما إذا كانت هناك اختلافات لغوية كبيرة يمكن أن تؤدي إلى سوء فهم أو صدمات ثقافية. تحقيقا لهذه الغاية، تتكون Corpus Compounts من البرنامج النصي الإسباني، المتاحة الترجمات الإنجليزية في Netflix والإصدار المترجم من البرنامج النصي. لتحليل البيانات، تم تصنيف الأخطاء بعد مصممة خطأ DQF / MQM وتم تقييمها باستخدام Metric Bleu التلقائي. تظهر النتائج أن محركات NMT تقدم ترجمات ذات نوعية جيدة، والتي بدورها قد تستفيد المترجمين الذين يعملون مع موارد الترفيه البصرية.
In recent years, the emergence of streaming platforms such as Netflix, HBO or Amazon Prime Video has reshaped the field of entertainment, which increasingly relies on subtitling, dubbing or voice-over modes. 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 in Netflix and the translated version of the script. For the analysis of the data, 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.
References used
https://aclanthology.org/
rate research
Read More
Production NMT systems typically need to serve niche domains that are not covered by adequately large and readily available parallel corpora. As a result, practitioners often fine-tune general purpose models to each of the domains their organisation
caters to. The number of domains however can often become large, which in combination with the number of languages that need serving can lead to an unscalable fleet of models to be developed and maintained. We propose Multi Dimensional Tagging, a method for fine-tuning a single NMT model on several domains simultaneously, thus drastically reducing development and maintenance costs. We run experiments where a single MDT model compares favourably to a set of SOTA specialist models, even when evaluated on the domain those baselines have been fine-tuned on. Besides BLEU, we report human evaluation results. MDT models are now live at Booking.com, powering an MT engine that serves millions of translations a day in over 40 different languages.
Low-resource languages can be understood as languages that are more scarce, less studied, less privileged, less commonly taught and for which there are less resources available (Singh, 2008; Cieri et al., 2016; Magueresse et al., 2020). Natural Langu
age Processing (NLP) research and technology mainly focuses on those languages for which there are large data sets available. To illustrate differences in data availability: there are 6 million Wikipedia articles available for English, 2 million for Dutch, and merely 82 thousand for Albanian. The scarce data issue becomes increasingly apparent when large parallel data sets are required for applications such as Neural Machine Translation (NMT). In this work, we investigate to what extent translation between Albanian (SQ) and Dutch (NL) is possible comparing a one-to-one (SQ↔AL) model, a low-resource pivot-based approach (English (EN) as pivot) and a zero-shot translation (ZST) (Johnson et al., 2016; Mattoni et al., 2017) system. From our experiments, it results that the EN-pivot-model outperforms both the direct one-to-one and the ZST model. Since often, small amounts of parallel data are available for low-resource languages or settings, experiments were conducted using small sets of parallel NL↔SQ data. The ZST appeared to be the worst performing models. Even when the available parallel data (NL↔SQ) was added, i.e. in a few-shot setting (FST), it remained the worst performing system according to the automatic (BLEU and TER) and human evaluation.
The audio-visual speech recognition systems that rely on speech and
movement of the lips of the speaker of the most important speech
recognition systems. Many different techniques have developed in
terms of the methods used in the feature extracti
on and classification
methods.
Research proposes the establishment of a system to identify isolated
words based audio features extracted from videos pronunciations of
words in Arabic in an environment free of noise, and then add the
energy and Temporal derivative components in extracting features of
the method Mel Frequency Cepstral Coefficient (MFCC) stage.
This paper describes the SEBAMAT contribution to the 2021 WMT Similar Language Translation shared task. Using the Marian neural machine translation toolkit, translation systems based on Google's transformer architecture were built in both directions
of Catalan--Spanish and Portuguese--Spanish. The systems were trained in two contrastive parameter settings (different vocabulary sizes for byte pair encoding) using only the parallel but not the comparable corpora provided by the shared task organizers. According to their official evaluation results, the SEBAMAT system turned out to be competitive with rankings among the top teams and BLEU scores between 38 and 47 for the language pairs involving Portuguese and between 76 and 80 for the language pairs involving Catalan.
This paper describes the submission to the WMT 2021 news translation shared task by the UPC Machine Translation group. The goal of the task is to translate German to French (De-Fr) and French to German (Fr-De). Our submission focuses on fine-tuning a
pre-trained model to take advantage of monolingual data. We fine-tune mBART50 using the filtered data, and additionally, we train a Transformer model on the same data from scratch. In the experiments, we show that fine-tuning mBART50 results in 31.69 BLEU for De-Fr and 23.63 BLEU for Fr-De, which increases 2.71 and 1.90 BLEU accordingly, as compared to the model we train from scratch. Our final submission is an ensemble of these two models, further increasing 0.3 BLEU for Fr-De.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
