Investigating the Impact of Language-Adaptive Fine-Tuning on Sentiment Analysis in Hausa Language Using AfriBERTa

• URL: http://arxiv.org/abs/2501.11023v1
• Date: Sun, 19 Jan 2025 11:52:46 GMT
• Title: Investigating the Impact of Language-Adaptive Fine-Tuning on Sentiment Analysis in Hausa Language Using AfriBERTa
• Authors: Sani Abdullahi Sani, Shamsuddeen Hassan Muhammad, Devon Jarvis,
• Abstract summary: Sentiment analysis (SA) plays a vital role in Natural Language Processing (NLP) by identifying sentiments expressed in text. This study investigates the effectiveness of Language-Adaptive Fine-Tuning (LAFT) to improve SA performance in Hausa.
• Score: 2.5055584842618175
• License:
• Abstract: Sentiment analysis (SA) plays a vital role in Natural Language Processing (NLP) by ~identifying sentiments expressed in text. Although significant advances have been made in SA for widely spoken languages, low-resource languages such as Hausa face unique challenges, primarily due to a lack of digital resources. This study investigates the effectiveness of Language-Adaptive Fine-Tuning (LAFT) to improve SA performance in Hausa. We first curate a diverse, unlabeled corpus to expand the model's linguistic capabilities, followed by applying LAFT to adapt AfriBERTa specifically to the nuances of the Hausa language. The adapted model is then fine-tuned on the labeled NaijaSenti sentiment dataset to evaluate its performance. Our findings demonstrate that LAFT gives modest improvements, which may be attributed to the use of formal Hausa text rather than informal social media data. Nevertheless, the pre-trained AfriBERTa model significantly outperformed models not specifically trained on Hausa, highlighting the importance of using pre-trained models in low-resource contexts. This research emphasizes the necessity for diverse data sources to advance NLP applications for low-resource African languages. We published the code and the dataset to encourage further research and facilitate reproducibility in low-resource NLP here: https://github.com/Sani-Abdullahi-Sani/Natural-Language-Processing/blob/main/Sentiment%20Analysis%20 for%20Low%20Resource%20African%20Languages

Related papers

• Evaluating Standard and Dialectal Frisian ASR: Multilingual Fine-tuning and Language Identification for Improved Low-resource Performance [9.624005980086707]
  State-of-the-art methods deploy self-supervised transfer learning where a model pre-trained on large amounts of data is fine-tuned using little labeled data. We show that Frisian ASR performance can be improved by using multilingual fine-tuning data and an auxiliary language identification task.
  arXiv  Detail & Related papers  (2025-02-07T12:42:46Z)
• A Multilingual Sentiment Lexicon for Low-Resource Language Translation using Large Languages Models and Explainable AI [0.0]
  South Africa and the DRC present a complex linguistic landscape with languages such as Zulu, Sepedi, Afrikaans, French, English, and Tshiluba. This study develops a multilingual lexicon designed for French and Tshiluba, now expanded to include translations in English, Afrikaans, Sepedi, and Zulu. A comprehensive testing corpus is created to support translation and sentiment analysis tasks, with machine learning models trained to predict sentiment.
  arXiv  Detail & Related papers  (2024-11-06T23:41:18Z)
• 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)
• Conversations in Galician: a Large Language Model for an Underrepresented Language [2.433983268807517]
  This paper introduces two novel resources designed to enhance Natural Language Processing (NLP) for the Galician language. We present a Galician adaptation of the Alpaca dataset, comprising 52,000 instructions and demonstrations. As a demonstration of the dataset utility, we fine-tuned LLaMA-7B to comprehend and respond in Galician, a language not originally supported by the model.
  arXiv  Detail & Related papers  (2023-11-07T08:52:28Z)
• Quantifying the Dialect Gap and its Correlates Across Languages [69.18461982439031]
  This work will lay the foundation for furthering the field of dialectal NLP by laying out evident disparities and identifying possible pathways for addressing them through mindful data collection.
  arXiv  Detail & Related papers  (2023-10-23T17:42:01Z)
• Arabic Sentiment Analysis with Noisy Deep Explainable Model [48.22321420680046]
  This paper proposes an explainable sentiment classification framework for the Arabic language. The proposed framework can explain specific predictions by training a local surrogate explainable model. We carried out experiments on public benchmark Arabic SA datasets.
  arXiv  Detail & Related papers  (2023-09-24T19:26:53Z)
• NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource Languages [54.808217147579036]
  We conduct a case study on Indonesian local languages. We compare the effectiveness of online scraping, human translation, and paragraph writing by native speakers in constructing datasets. Our findings demonstrate that datasets generated through paragraph writing by native speakers exhibit superior quality in terms of lexical diversity and cultural content.
  arXiv  Detail & Related papers  (2023-09-19T14:42:33Z)
• Can Character-based Language Models Improve Downstream Task Performance in Low-Resource and Noisy Language Scenarios? [0.0]
  We focus on North-African colloquial dialectal Arabic written using an extension of the Latin script, called NArabizi. We show that a character-based model trained on only 99k sentences of NArabizi and fined-tuned on a small treebank leads to performance close to those obtained with the same architecture pre-trained on large multilingual and monolingual models.
  arXiv  Detail & Related papers  (2021-10-26T14:59:16Z)
• Low-Resource Language Modelling of South African Languages [6.805575417034369]
  We evaluate the performance of open-vocabulary language models on low-resource South African languages. We evaluate different variants of n-gram models, feedforward neural networks, recurrent neural networks (RNNs) and Transformers on small-scale datasets. Overall, well-regularized RNNs give the best performance across two isiZulu and one Sepedi datasets.
  arXiv  Detail & Related papers  (2021-04-01T21:27:27Z)
• Unsupervised Domain Adaptation of a Pretrained Cross-Lingual Language Model [58.27176041092891]
  Recent research indicates that pretraining cross-lingual language models on large-scale unlabeled texts yields significant performance improvements. We propose a novel unsupervised feature decomposition method that can automatically extract domain-specific features from the entangled pretrained cross-lingual representations. Our proposed model leverages mutual information estimation to decompose the representations computed by a cross-lingual model into domain-invariant and domain-specific parts.
  arXiv  Detail & Related papers  (2020-11-23T16:00:42Z)
• Building Low-Resource NER Models Using Non-Speaker Annotation [58.78968578460793]
  Cross-lingual methods have had notable success in addressing these concerns. We propose a complementary approach to building low-resource Named Entity Recognition (NER) models using non-speaker'' (NS) annotations. We show that use of NS annotators produces results that are consistently on par or better than cross-lingual methods built on modern contextual representations.
  arXiv  Detail & Related papers  (2020-06-17T03:24:38Z)

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.