NER- RoBERTa: Fine-Tuning RoBERTa for Named Entity Recognition (NER) within low-resource languages

• URL: http://arxiv.org/abs/2412.15252v1
• Date: Sun, 15 Dec 2024 07:07:17 GMT
• Title: NER- RoBERTa: Fine-Tuning RoBERTa for Named Entity Recognition (NER) within low-resource languages
• Authors: Abdulhady Abas Abdullah, Srwa Hasan Abdulla, Dalia Mohammad Toufiq, Halgurd S. Maghdid, Tarik A. Rashid, Pakshan F. Farho, Shadan Sh. Sabr, Akar H. Taher, Darya S. Hamad, Hadi Veisi, Aras T. Asaad,
• Abstract summary: This work proposes a methodology for fine-tuning the pre-trained RoBERTa model for Kurdish NER (KNER) Experiments show that fine-tuned RoBERTa with the SentencePiece tokenization method substantially improves KNER performance.
• Score: 3.5403652483328223
• License:
• Abstract: Nowadays, Natural Language Processing (NLP) is an important tool for most people's daily life routines, ranging from understanding speech, translation, named entity recognition (NER), and text categorization, to generative text models such as ChatGPT. Due to the existence of big data and consequently large corpora for widely used languages like English, Spanish, Turkish, Persian, and many more, these applications have been developed accurately. However, the Kurdish language still requires more corpora and large datasets to be included in NLP applications. This is because Kurdish has a rich linguistic structure, varied dialects, and a limited dataset, which poses unique challenges for Kurdish NLP (KNLP) application development. While several studies have been conducted in KNLP for various applications, Kurdish NER (KNER) remains a challenge for many KNLP tasks, including text analysis and classification. In this work, we address this limitation by proposing a methodology for fine-tuning the pre-trained RoBERTa model for KNER. To this end, we first create a Kurdish corpus, followed by designing a modified model architecture and implementing the training procedures. To evaluate the trained model, a set of experiments is conducted to demonstrate the performance of the KNER model using different tokenization methods and trained models. The experimental results show that fine-tuned RoBERTa with the SentencePiece tokenization method substantially improves KNER performance, achieving a 12.8% improvement in F1-score compared to traditional models, and consequently establishes a new benchmark for KNLP.

Related papers

• One Model is All You Need: ByT5-Sanskrit, a Unified Model for Sanskrit NLP Tasks [26.848664285007022]
  ByT5-Sanskrit is designed for NLP applications involving the morphologically rich language Sanskrit. It is easier to deploy and more robust to data not covered by external linguistic resources. We show that our approach yields new best scores for lemmatization and dependency parsing of other morphologically rich languages.
  arXiv  Detail & Related papers  (2024-09-20T22:02:26Z)
• A Novel Cartography-Based Curriculum Learning Method Applied on RoNLI: The First Romanian Natural Language Inference Corpus [71.77214818319054]
  Natural language inference is a proxy for natural language understanding. There is no publicly available NLI corpus for the Romanian language. We introduce the first Romanian NLI corpus (RoNLI) comprising 58K training sentence pairs.
  arXiv  Detail & Related papers  (2024-05-20T08:41:15Z)
• Bridging the Bosphorus: Advancing Turkish Large Language Models through Strategies for Low-Resource Language Adaptation and Benchmarking [1.3716808114696444]
  Large Language Models (LLMs) are becoming crucial across various fields, emphasizing the urgency for high-quality models in underrepresented languages. This study explores the unique challenges faced by low-resource languages, such as data scarcity, model selection, evaluation, and computational limitations.
  arXiv  Detail & Related papers  (2024-05-07T21:58:45Z)
• CMULAB: An Open-Source Framework for Training and Deployment of Natural Language Processing Models [59.91221728187576]
  This paper introduces the CMU Linguistic Linguistic Backend, an open-source framework that simplifies model deployment and continuous human-in-the-loop fine-tuning of NLP models. CMULAB enables users to leverage the power of multilingual models to quickly adapt and extend existing tools for speech recognition, OCR, translation, and syntactic analysis to new languages.
  arXiv  Detail & Related papers  (2024-04-03T02:21:46Z)
• 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 dialectal 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)
• Improving Natural Language Inference in Arabic using Transformer Models and Linguistically Informed Pre-Training [0.34998703934432673]
  This paper addresses the classification of Arabic text data in the field of Natural Language Processing (NLP) To overcome this limitation, we create a dedicated data set from publicly available resources. We find that a language-specific model (AraBERT) performs competitively with state-of-the-art multilingual approaches.
  arXiv  Detail & Related papers  (2023-07-27T07:40:11Z)
• Comparison of Pre-trained Language Models for Turkish Address Parsing [0.0]
  We focus on Turkish maps data and thoroughly evaluate both multilingual and Turkish based BERT, DistilBERT, ELECTRA and RoBERTa. We also propose a MultiLayer Perceptron (MLP) for fine-tuning BERT in addition to the standard approach of one-layer fine-tuning.
  arXiv  Detail & Related papers  (2023-06-24T12:09:43Z)
• Pre-Training to Learn in Context [138.0745138788142]
  The ability of in-context learning is not fully exploited because language models are not explicitly trained to learn in context. We propose PICL (Pre-training for In-Context Learning), a framework to enhance the language models' in-context learning ability. Our experiments show that PICL is more effective and task-generalizable than a range of baselines, outperforming larger language models with nearly 4x parameters.
  arXiv  Detail & Related papers  (2023-05-16T03:38:06Z)
• DaCy: A Unified Framework for Danish NLP [1.2891210250935146]
  We present DaCy: a unified framework for Danish NLP built on SpaCy. DaCy uses efficient models which obtain state-of-the-art performance on named entity recognition, part-of-speech tagging, and dependency parsing. We conduct a series of tests for biases and robustness of Danish NLP pipelines through augmentation of the test set of DaNE.
  arXiv  Detail & Related papers  (2021-07-12T10:14:31Z)
• 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)
• Coreferential Reasoning Learning for Language Representation [88.14248323659267]
  We present CorefBERT, a novel language representation model that can capture the coreferential relations in context. The experimental results show that, compared with existing baseline models, CorefBERT can achieve significant improvements consistently on various downstream NLP tasks.
  arXiv  Detail & Related papers  (2020-04-15T03:57:45Z)

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.