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Relevant bibliographies by topics / Low-resource language settings

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Academic literature on the topic 'Low-resource language settings'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 February 2022

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Journal articles on the topic "Low-resource language settings"

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Lee, Chanhee, Kisu Yang, Taesun Whang, Chanjun Park, Andrew Matteson, and Heuiseok Lim. "Exploring the Data Efficiency of Cross-Lingual Post-Training in Pretrained Language Models." Applied Sciences 11, no. 5 (2021): 1974. http://dx.doi.org/10.3390/app11051974.

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Language model pretraining is an effective method for improving the performance of downstream natural language processing tasks. Even though language modeling is unsupervised and thus collecting data for it is relatively less expensive, it is still a challenging process for languages with limited resources. This results in great technological disparity between high- and low-resource languages for numerous downstream natural language processing tasks. In this paper, we aim to make this technology more accessible by enabling data efficient training of pretrained language models. It is achieved b

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Jayawardena, A., B. Waller, B. Edwards, et al. "Portable audiometric screening platforms used in low-resource settings: a review." Journal of Laryngology & Otology 133, no. 2 (2018): 74–79. http://dx.doi.org/10.1017/s0022215118001925.

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AbstractBackgroundMillions of people across the world suffer from disabling hearing loss. Appropriate interventions lead to improved speech and language skills, educational advancement, and improved social integration. A major limitation to improving care is identifying those with disabling hearing loss in low-resource countries.ObjectivesThis review article summarises information on currently available hearing screening platforms and technology available from published reports and the authors’ personal experiences of hearing loss identification in low-resource areas of the world. The paper re

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Adjeisah, Michael, Guohua Liu, Douglas Omwenga Nyabuga, Richard Nuetey Nortey, and Jinling Song. "Pseudotext Injection and Advance Filtering of Low-Resource Corpus for Neural Machine Translation." Computational Intelligence and Neuroscience 2021 (April 11, 2021): 1–10. http://dx.doi.org/10.1155/2021/6682385.

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Scaling natural language processing (NLP) to low-resourced languages to improve machine translation (MT) performance remains enigmatic. This research contributes to the domain on a low-resource English-Twi translation based on filtered synthetic-parallel corpora. It is often perplexing to learn and understand what a good-quality corpus looks like in low-resource conditions, mainly where the target corpus is the only sample text of the parallel language. To improve the MT performance in such low-resource language pairs, we propose to expand the training data by injecting synthetic-parallel corp

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Sandler, Mykayla L., Nohamin Ayele, Isaie Ncogoza, et al. "Improving Tracheostomy Care in Resource-Limited Settings." Annals of Otology, Rhinology & Laryngology 129, no. 2 (2019): 181–90. http://dx.doi.org/10.1177/0003489419882972.

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Objectives: Tracheostomy care in leading pediatric hospitals is both multidisciplinary and comprehensive, including generalized care protocols and thorough family training programs. This level of care is more difficult in resource-limited settings lacking developed healthcare infrastructure and tracheostomy education among nursing and resident staff. The objective of this study was to improve pediatric tracheostomy care in resource-limited settings. Methods: In collaboration with a team of otolaryngologists, respiratory therapists, tracheostomy nurses, medical illustrators, and global health e

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Chi, Zewen, Li Dong, Furu Wei, Wenhui Wang, Xian-Ling Mao, and Heyan Huang. "Cross-Lingual Natural Language Generation via Pre-Training." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 7570–77. http://dx.doi.org/10.1609/aaai.v34i05.6256.

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In this work we focus on transferring supervision signals of natural language generation (NLG) tasks between multiple languages. We propose to pretrain the encoder and the decoder of a sequence-to-sequence model under both monolingual and cross-lingual settings. The pre-training objective encourages the model to represent different languages in the shared space, so that we can conduct zero-shot cross-lingual transfer. After the pre-training procedure, we use monolingual data to fine-tune the pre-trained model on downstream NLG tasks. Then the sequence-to-sequence model trained in a single lang

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ZENNAKI, O., N. SEMMAR, and L. BESACIER. "A neural approach for inducing multilingual resources and natural language processing tools for low-resource languages." Natural Language Engineering 25, no. 1 (2018): 43–67. http://dx.doi.org/10.1017/s1351324918000293.

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AbstractThis work focuses on the rapid development of linguistic annotation tools for low-resource languages (languages that have no labeled training data). We experiment with several cross-lingual annotation projection methods using recurrent neural networks (RNN) models. The distinctive feature of our approach is that our multilingual word representation requires only a parallel corpus between source and target languages. More precisely, our approach has the following characteristics: (a) it does not use word alignment information, (b) it does not assume any knowledge about target languages

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Van der Heijden, Niels, Samira Abnar, and Ekaterina Shutova. "A Comparison of Architectures and Pretraining Methods for Contextualized Multilingual Word Embeddings." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 9090–97. http://dx.doi.org/10.1609/aaai.v34i05.6443.

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The lack of annotated data in many languages is a well-known challenge within the field of multilingual natural language processing (NLP). Therefore, many recent studies focus on zero-shot transfer learning and joint training across languages to overcome data scarcity for low-resource languages. In this work we (i) perform a comprehensive comparison of state-of-the-art multilingual word and sentence encoders on the tasks of named entity recognition (NER) and part of speech (POS) tagging; and (ii) propose a new method for creating multilingual contextualized word embeddings, compare it to multi

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Saleh, Shadi, Nour El Arnaout, Lina Abdouni, Zeinab Jammoul, Noha Hachach, and Amlan Dasgupta. "Sijilli: A Scalable Model of Cloud-Based Electronic Health Records for Migrating Populations in Low-Resource Settings." Journal of Medical Internet Research 22, no. 8 (2020): e18183. http://dx.doi.org/10.2196/18183.

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The world is witnessing an alarming rate of displacement and migration, with more than 70.8 million forcibly displaced individuals, including 26 million refugees. These populations are known to have increased vulnerability and susceptibility to mental and physical health problems due to the migration journey. Access of these individuals to health services, whether during their trajectory of displacement or in refugee-hosting countries, remains limited and challenging due to multiple factors, including language and cultural barriers and unavailability of the refugees’ health records. Cloud-base

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Arinitwe, Richard, Alice Willson, Sean Batenhorst, and Peter T. Cartledge. "Using a Global Health Media Project Video to Increase Knowledge and Confidence in the Mothers of Admitted Neonates in Rwanda: A Prospective Interventional Study." Journal of Tropical Pediatrics 66, no. 2 (2019): 136–43. http://dx.doi.org/10.1093/tropej/fmz042.

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Abstract Introduction In resource-limited settings, the ratio of trained health care professionals to admitted neonates is low. Parents therefore, frequently need to provide primary neonatal care. In order to do so safely, they require effective education and confidence. The evolution and availability of technology mean that video education is becoming more readily available in this setting. Aim This study aimed to investigate whether showing a short video on a specific neonatal topic could change the knowledge and confidence of mothers of admitted neonates. Methods A prospective interventiona

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Jorge, Ahmed, Michael D. White, and Nitin Agarwal. "Outcomes in socioeconomically disadvantaged patients with spinal cord injury: a systematic review." Journal of Neurosurgery: Spine 29, no. 6 (2018): 680–86. http://dx.doi.org/10.3171/2018.5.spine171242.

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OBJECTIVEIndividuals with a spinal cord injury (SCI) in socioeconomically disadvantaged settings (e.g., rural or low income) have different outcomes than their counterparts; however, a contemporary literature review identifying and measuring these outcomes has not been published. Here, the authors’ aim was to perform a systematic review and identify these parameters in the hope of providing tangible targets for future clinical research efforts.METHODSA systematic review was performed to find English-language articles published from 2007 to 2017 in the PubMed/MEDLINE, EMBASE, and SCOPUS databas

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Dissertations / Theses on the topic "Low-resource language settings"

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Black, Kevin P. "Interactive Machine Assistance: A Case Study in Linking Corpora and Dictionaries." BYU ScholarsArchive, 2015. https://scholarsarchive.byu.edu/etd/5620.

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Machine learning can provide assistance to humans in making decisions, including linguistic decisions such as determining the part of speech of a word. Supervised machine learning methods derive patterns indicative of possible labels (decisions) from annotated example data. For many problems, including most language analysis problems, acquiring annotated data requires human annotators who are trained to understand the problem and to disambiguate among multiple possible labels. Hence, the availability of experts can limit the scope and quantity of annotated data. Machine-learned pre-annotation

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Books on the topic "Low-resource language settings"

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Saugera, Valérie. Remade in France. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190625542.001.0001.

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Remade in France: Anglicisms in the Lexicon and Morphology of French chronicles the current status of French Anglicisms, a hot topic in the history of the French language and a compelling example of the influence of global English. The abundant data come from primary sources—a large online newspaper corpus (for unofficial Anglicisms) and the dictionary (for official Anglicisms)—and secondary sources. This book examines the appearance and behavior of English items in the lexicon and morphology of French, and explains them in the context of French neology and lexical activity. The first phase of

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Book chapters on the topic "Low-resource language settings"

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Srivastava, Brij Mohan Lal, and Manish Shrivastava. "Articulatory Gesture Rich Representation Learning of Phonological Units in Low Resource Settings." In Statistical Language and Speech Processing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45925-7_7.

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Ocan, Johnson. "Enhancing the English Language Ability of Postgraduate Research Students." In Postgraduate Research Engagement in Low Resource Settings. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0264-8.ch005.

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The chapter discusses opinions about grammar as a prescriptive diction in academic writing. It also argues that the problem of personal pronouns can be used to analyze the language used by post-graduate students in low-resource setting and others whether in speech or writing, in non-literally discourse or literature. The chapter analyzes four maxims of good writing: Make your language easy to follow; be clear; be economical; and be effective. To successfully create knowledge, especially at postgraduate level, authors must communicate concisely to present their sense.

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Tiedemann, Jörg. "The Development of a Comprehensive Data Set for Systematic Studies of Machine Translation." In Multilingual Facilitation. University of Helsinki, 2021. http://dx.doi.org/10.31885/9789515150257.22.

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This paper presents our on-going efforts to develop a comprehensive data set and benchmark for machine translation beyond high-resource languages. The current release includes 500GB of compressed parallel data for almost 3,000 language pairs covering over 500 languages and language variants. We present the structure of the data set and demonstrate its use for systematic studies based on baseline experiments with multilingual neural machine translation between Finno-Ugric languages and other language groups. Our initial results show the capabilities of training effective multilingual translation models with skewed training data but also stress the shortcomings with low-resource settings and the difficulties to obtain sufficient information through straightforward transfer from related languages.

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Conference papers on the topic "Low-resource language settings"

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Dey, Anik, Weibin Zhang, and Pascale Fung. "Acoustic modeling for hindi speech recognition in low-resource settings." In 2014 International Conference on Audio, Language and Image Processing (ICALIP). IEEE, 2014. http://dx.doi.org/10.1109/icalip.2014.7009923.

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Melas-Kyriazi, Luke, George Han, and Celine Liang. "Generation-Distillation for Efficient Natural Language Understanding in Low-Data Settings." In Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019). Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/d19-6114.

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Kalimuthu, Marimuthu, Michael Barz, and Daniel Sonntag. "Incremental Domain Adaptation for Neural Machine Translation in Low-Resource Settings." In Proceedings of the Fourth Arabic Natural Language Processing Workshop. Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/w19-4601.

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Samarakoon, Lahiru, Brian Mak, and Albert Y. S. Lam. "Domain Adaptation of End-to-end Speech Recognition in Low-Resource Settings." In 2018 IEEE Spoken Language Technology Workshop (SLT). IEEE, 2018. http://dx.doi.org/10.1109/slt.2018.8639506.

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Schulz, Claudia, Steffen Eger, Johannes Daxenberger, Tobias Kahse, and Iryna Gurevych. "Multi-Task Learning for Argumentation Mining in Low-Resource Settings." In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers). Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/n18-2006.

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Molapo, Maletsabisa, Chane Simone Moodley, Ismail Yunus Akhalwaya, Toby Kurien, Jay Kloppenberg, and Richard Young. "Designing Digital Peer Assessment for Second Language Learning in Low Resource Learning Settings." In L@S '19: Sixth (2019) ACM Conference on Learning @ Scale. ACM, 2019. http://dx.doi.org/10.1145/3330430.3333626.

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Bharadwaj, Akash, David Mortensen, Chris Dyer, and Jaime Carbonell. "Phonologically Aware Neural Model for Named Entity Recognition in Low Resource Transfer Settings." In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2016. http://dx.doi.org/10.18653/v1/d16-1153.

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Oguz, Cennet, and Ngoc Thang Vu. "A Two-stage Model for Slot Filling in Low-resource Settings: Domain-agnostic Non-slot Reduction and Pretrained Contextual Embeddings." In Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing. Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.sustainlp-1.10.

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Moeller, Sarah, Ling Liu, and Mans Hulden. "To POS Tag or Not to POS Tag: The Impact of POS Tags on Morphological Learning in Low-Resource Settings." In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.acl-long.78.

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Li, Juntao, Ruidan He, Hai Ye, Hwee Tou Ng, Lidong Bing, and Rui Yan. "Unsupervised Domain Adaptation of a Pretrained Cross-Lingual Language Model." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/508.

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Recent research indicates that pretraining cross-lingual language models on large-scale unlabeled texts yields significant performance improvements over various cross-lingual and low-resource tasks. Through training on one hundred languages and terabytes of texts, cross-lingual language models have proven to be effective in leveraging high-resource languages to enhance low-resource language processing and outperform monolingual models. In this paper, we further investigate the cross-lingual and cross-domain (CLCD) setting when a pretrained cross-lingual language model needs to adapt to new dom

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