Search Results (5)

Kreol Morisien is a vibrant and expressive language that reflects the multicultural heritage of Mauritius. There are different versions of Kreol languages. While Kreol Morisien is spoken in Mauritius, Kreol Rodrige is spoken only in Rodrigues, and they are distinct languages. Being spoken by only about 1.5 million speakers in the world, Kreol Morisien falls in the category of under-resourced languages. Initially, Kreol Morisien lacked a formalised writing system, with many people using different spellings for the same words. The first step towards standardisation of writing Kreol Morisien was after the publication of the Kreol Morisien orthography in 2011 and Kreol Morisien grammar in 2012 by the Kreol Morisien Academy. Kreol Morisien obtained a national position in the year 2012 when it was introduced in educational organisations. This was a major breakthrough for Kreol Morisien to be recognised as a national language on the same level as English, French, and other oriental languages. By providing a means for Kreol Morisien speakers to connect with others, a translation system will help to preserve and strengthen the identity of the language and its speakers in an increasingly globalized world. The aim of this paper is to develop a translation system for Kreol Morisien and English. Thus, a dataset consisting of 50,000 parallel Kreol Morisien and English sentences was created, where 48,000 sentence pairs were used to train the models, while 1000 sentences were used for evaluation and another 1000 sentences were used for testing. Several machine translation systems such as statistical machine translation, open-source neural machine translation, a Transformer model with attention mechanism, and Marian machine translation are trained and evaluated. Our best model, using MarianMT, achieved a BLEU score of 0.62 for the translation of English to Kreol Morisien and a BLEU score of 0.58 for the translation of Kreol Morisien into English. To our knowledge, these are the highest BLEU scores that are available in the literature for this language pair. A high-quality translation tool for Kreol Morisien will facilitate its integration into digital platforms. This will make previously inaccessible knowledge more accessible, as the information can now be translated into the mother tongue of most Mauritians with reasonable accuracy. Full article

This paper investigates the domain of Natural Language Inference (NLI), with an emphasis on Recognizing Textual Entailment (RTE). We utilize the Stanford Natural Language Inference (SNLI) dataset, a benchmark for RTE tasks, to examine the efficacy of machine back translation and model performance in textual entailment. Our methodology employs a cost-effective approach using an open-source machine translation library like MarianMT with Helsinki-NLP/opus-mt models for back translation, applied to the comprehensive SNLI dataset. The concluding analysis demonstrates that no single model, whether back translated or augmented, consistently outperforms the reference English model in all aspects. The performance variations are particular to certain word overlap ranges and categories, suggesting that these models are essentially equivalent to the reference. This study contributes to the comprehension of machine translation′s impact on textual entailment models, emphasizing the complexities in multilingual NLI tasks. Full article

Sign language serves as the primary mode of communication for the deaf community. With technological advancements, it is crucial to develop systems capable of enhancing communication between deaf and hearing individuals. This paper reviews recent state-of-the-art methods in sign language recognition, translation, and production. Additionally, we introduce a rule-based system, called ruLSE, for generating synthetic datasets in Spanish Sign Language. To check the usefulness of these datasets, we conduct experiments with two state-of-the-art models based on Transformers, MarianMT and Transformer-STMC. In general, we observe that the former achieves better results (+3.7 points in the BLEU-4 metric) although the latter is up to four times faster. Furthermore, the use of pre-trained word embeddings in Spanish enhances results. The rule-based system demonstrates superior performance and efficiency compared to Transformer models in Sign Language Production tasks. Lastly, we contribute to the state of the art by releasing the generated synthetic dataset in Spanish named synLSE. Full article

The Norwegian business-to-business (B2B) market for bicycles consists mainly of international brands, such as Shimano, Trek, Cannondale, and Specialized. The product descriptions for these brands are usually in English and need local translation. However, these product descriptions include bicycle-specific terminologies that are challenging for online translators, such as Google. For this reason, local companies outsource translation or translate product descriptions manually, which is cumbersome. In light of the Norwegian B2B bicycle industry, this paper explores transfer learning to improve the machine translation of bicycle-specific terminology from English to Norwegian, including generic text. Firstly, we trained a custom Named-Entity Recognition (NER) model to identify cycling-specific terminology and then adapted a MarianMT neural machine translation model for the translation process. Due to the lack of publicly available bicycle-terminology-related datasets to train the proposed models, we created our dataset by collecting a corpus of cycling-related texts. We evaluated the performance of our proposed model and compared its performance with that of Google Translate. Our model outperformed Google Translate on the test set, with a SacreBleu score of 45.099 against 36.615 for Google Translate on average. We also created a web application where the user can input English text with related bicycle terminologies, and it will return the detected cycling-specific words in addition to a Norwegian translation. Full article

Mother Mary has been and can be a spiritual role model for Korean Catholics to live out their faith, a faith that is based on a dynamic understanding of a spiritual model of Mary, that is, spiritual growth coming from Eastern Mariology, particularly Mariology based on Gregory Palamas. For this dynamic understanding of Marian spirituality, I review the Scriptures on Mary, including Luke, and Palamas’ reflection on Mary’s spiritual life. This dynamic understanding of Mary and Her spiritual life never contradicts the static approach to Mariology, the Immaculate Conception, through which Jesus, who is God, was born. I believe Mother Mary in Heaven still loves Her Son and God, and Her love is becoming deeper and deeper in Heaven, which means that some virtues, such as wisdom, knowledge, prudence, vigilance, and endurance, could be interim virtues “until heaven and earth pass away” (Mt 5:19), for love and intimacy with God are permanent in Heaven. This is the belief that we can grow our love continuously with Mary forever. Full article