Recent Advancements and Challenges of Turkic Central Asian Language Processing

• URL: http://arxiv.org/abs/2407.05006v1
• Date: Sat, 6 Jul 2024 08:58:26 GMT
• Title: Recent Advancements and Challenges of Turkic Central Asian Language Processing
• Authors: Yana Veitsman,
• Abstract summary: This paper focuses on the NLP sphere of the Turkic counterparts of Central Asian languages, namely Kazakh, Uzbek, Kyrgyz, and Turkmen. It gives a broad, high-level overview of the linguistic properties of the languages, the current coverage and performance of already developed technology, and availability of labeled and unlabeled data for each language.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Research in the NLP sphere of the Turkic counterparts of Central Asian languages, namely Kazakh, Uzbek, Kyrgyz, and Turkmen, comes with the typical challenges of low-resource languages, like data scarcity and a general lack of linguistic resources. However, in the recent years research has greatly advanced via collection of language-specific datasets and development of downstream task technologies. Aiming to summarize this research up until May 2024, this paper also seeks to identify potential areas of future work. To achieve this, the paper gives a broad, high-level overview of the linguistic properties of the languages, the current coverage and performance of already developed technology, application of transfer learning techniques from higher-resource languages, and availability of labeled and unlabeled data for each language. Providing a summary of the current state of affairs, we hope that further research will be facilitated with the considerations we provide in the current paper.

Related papers

• Monolingual and Multilingual Misinformation Detection for Low-Resource Languages: A Comprehensive Survey [2.5459710368096586]
  This survey provides a comprehensive overview of the current research on low-resource language misinformation detection. We review the existing datasets, methodologies, and tools used in these domains, identifying key challenges related to: data resources, model development, cultural and linguistic context, real-world applications, and research efforts. Our findings underscore the need for robust, inclusive systems capable of addressing misinformation across diverse linguistic and cultural contexts.
  arXiv  Detail & Related papers  (2024-10-24T03:02:03Z)
• A Survey on Large Language Models with Multilingualism: Recent Advances and New Frontiers [48.314619377988436]
  The rapid development of Large Language Models (LLMs) demonstrates remarkable multilingual capabilities in natural language processing. Despite the breakthroughs of LLMs, the investigation into the multilingual scenario remains insufficient. This survey aims to help the research community address multilingual problems and provide a comprehensive understanding of the core concepts, key techniques, and latest developments in multilingual natural language processing based on LLMs.
  arXiv  Detail & Related papers  (2024-05-17T17:47:39Z)
• The Ghanaian NLP Landscape: A First Look [9.17372840572907]
  Ghanaian languages, in particular, face an alarming decline, with documented extinction and several at risk. This study pioneers a comprehensive survey of Natural Language Processing (NLP) research focused on Ghanaian languages.
  arXiv  Detail & Related papers  (2024-05-10T21:39:09Z)
• Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontiers [81.47046536073682]
  We present a review and provide a unified perspective to summarize the recent progress as well as emerging trends in multilingual large language models (MLLMs) literature. We hope our work can provide the community with quick access and spur breakthrough research in MLLMs.
  arXiv  Detail & Related papers  (2024-04-07T11:52:44Z)
• Natural Language Processing for Dialects of a Language: A Survey [56.93337350526933]
  State-of-the-art natural language processing (NLP) models are trained on massive training corpora, and report a superlative performance on evaluation datasets. This survey delves into an important attribute of these datasets: the dialect of a language. Motivated by the performance degradation of NLP models for dialectic datasets and its implications for the equity of language technologies, we survey past research in NLP for dialects in terms of datasets, and approaches.
  arXiv  Detail & Related papers  (2024-01-11T03:04:38Z)
• NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages [100.59889279607432]
  We focus on developing resources for languages in Indonesia. Most languages in Indonesia are categorized as endangered and some are even extinct. We develop the first-ever parallel resource for 10 low-resource languages in Indonesia.
  arXiv  Detail & Related papers  (2022-05-31T17:03:50Z)
• Deep Transfer Learning & Beyond: Transformer Language Models in Information Systems Research [0.913755431537592]
  Recent progress in natural language processing involving transformer language models (TLMs) offers a potential avenue for AI-driven business and societal transformation. We review this recent progress as well as recent literature utilizing text mining in top IS journals to develop an outline for how future IS research can benefit from these new techniques.
  arXiv  Detail & Related papers  (2021-10-18T02:01:39Z)
• Systematic Inequalities in Language Technology Performance across the World's Languages [94.65681336393425]
  We introduce a framework for estimating the global utility of language technologies. Our analyses involve the field at large, but also more in-depth studies on both user-facing technologies and more linguistic NLP tasks.
  arXiv  Detail & Related papers  (2021-10-13T14:03:07Z)
• Reinforced Iterative Knowledge Distillation for Cross-Lingual Named Entity Recognition [54.92161571089808]
  Cross-lingual NER transfers knowledge from rich-resource language to languages with low resources. Existing cross-lingual NER methods do not make good use of rich unlabeled data in target languages. We develop a novel approach based on the ideas of semi-supervised learning and reinforcement learning.
  arXiv  Detail & Related papers  (2021-06-01T05:46:22Z)
• Low resource language dataset creation, curation and classification: Setswana and Sepedi -- Extended Abstract [2.3801001093799115]
  We create datasets that are focused on news headlines for Setswana and Sepedi. We propose baselines for classification, and investigate an approach on data augmentation better suited to low-resourced languages.
  arXiv  Detail & Related papers  (2020-03-30T18:03:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.