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Register a new userT4T Solution: WMT21 Similar Language Task for the Spanish-Catalan and Spanish-Portuguese Language Pair
حل T4T: WMT21 مهمة لغة مماثلة لزوج اللغة الإسبانية الكاتالونية والإسبانية والبرتغالية
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تم التركيز الفكرة الرئيسية لهذا الحل على التركيز على تنظيف Corpus وإعدادها وبعد ذلك، استخدم حل خارج مربع (OpenNMT) مع طراز المحولات المنشور الافتراضي.لإعداد Corpus، استخدمنا مجموعة من الأدوات القياسية (كبرامج نصية موسى أو حزم بيثون)، ولكن أيضا، من بين البرامج النصية الثابتة الأخرى، ومزخرفة مخصصة لبثون مع القدرة على استبدال الأرقام للمتغيرات، حل مشكلة العلوي / السفلي للحالةالمفردات وتوفير تجزئة جيدة لمعظم علامات الترقيم.لقد بدأنا أيضا خطا لتنظيف Corpus بناء على تقدير الاحتمالات الإحصائية ل Corpus المستهدف المصدر، مع نتائج غير واضحة.أيضا، لقد قمت بتشغيل بعض الاختبارات مع تجزئة الكلمات المقاطعة، مرة أخرى بنتائج غير واضحة، لذلك في النهاية، بعد تكتيح جملة Word، استخدمنا جملة BPE الخاصة بوحدات الكلمات الفرعية لتغذية OpenNMT.
The main idea of this solution has been to focus on corpus cleaning and preparation and after that, use an out of box solution (OpenNMT) with its default published transformer model. To prepare the corpus, we have used set of standard tools (as Moses scripts or python packages), but also, among other python scripts, a python custom tokenizer with the ability to replace numbers for variables, solve the upper/lower case issue of the vocabulary and provide good segmentation for most of the punctuation. We also have started a line to clean corpus based on statistical probability estimation of source-target corpus, with unclear results. Also, we have run some tests with syllabical word segmentation, again with unclear results, so at the end, after word sentence tokenization we have used BPE SentencePiece for subword units to feed OpenNMT.
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https://aclanthology.org/
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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.
Offensive language detection and analysis has become a major area of research in Natural Language Processing. The freedom of participation in social media has exposed online users to posts designed to denigrate, insult or hurt them according to gende
r, race, religion, ideology, or other personal characteristics. Focusing on young influencers from the well-known social platforms of Twitter, Instagram, and YouTube, we have collected a corpus composed of 47,128 Spanish comments manually labeled on offensive pre-defined categories. A subset of the corpus attaches a degree of confidence to each label, so both multi-class classification and multi-output regression studies are possible. In this paper, we introduce the corpus, discuss its building process, novelties, and some preliminary experiments with it to serve as a baseline for the research community.
In this work, two Neural Machine Translation (NMT) systems have been developed and evaluated as part of the bidirectional Tamil-Telugu similar languages translation subtask in WMT21. The OpenNMT-py toolkit has been used to create quick prototypes of
the systems, following which models have been trained on the training datasets containing the parallel corpus and finally the models have been evaluated on the dev datasets provided as part of the task. Both the systems have been trained on a DGX station with 4 -V100 GPUs. The first NMT system in this work is a Transformer based 6 layer encoder-decoder model, trained for 100000 training steps, whose configuration is similar to the one provided by OpenNMT-py and this is used to create a model for bidirectional translation. The second NMT system contains two unidirectional translation models with the same configuration as the first system, with the addition of utilizing Byte Pair Encoding (BPE) for subword tokenization through the pre-trained MultiBPEmb model. Based on the dev dataset evaluation metrics for both the systems, the first system i.e. the vanilla Transformer model has been submitted as the Primary system. Since there were no improvements in the metrics during training of the second system with BPE, it has been submitted as a contrastive system.
We investigate transfer learning based on pre-trained neural machine translation models to translate between (low-resource) similar languages. This work is part of our contribution to the WMT 2021 Similar Languages Translation Shared Task where we su
bmitted models for different language pairs, including French-Bambara, Spanish-Catalan, and Spanish-Portuguese in both directions. Our models for Catalan-Spanish (82.79 BLEU)and Portuguese-Spanish (87.11 BLEU) rank top 1 in the official shared task evaluation, and we are the only team to submit models for the French-Bambara pairs.
This paper is concerned with the theme of divine
justice in The Spanish Tragedy (1592) by Thomas Kyd
(1558-1594), a famous English Elizabethan dramatist. It
first defines the term "didactic", and then moves to
discuss the Portuguese playlet in Th
e Spanish Tragedy
as a miniature play-within-the-play which reveals the
issue of divine judgment. This paper concludes by
asserting that this play is a tragedy of divine justice and
punishment.
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