Masakhane-Afrisenti at SemEval-2023 Task 12: Sentiment Analysis using Afro-centric Language Models and Adapters for Low-resource African Languages

• URL: http://arxiv.org/abs/2304.06459v1
• Date: Thu, 13 Apr 2023 12:54:29 GMT
• Title: Masakhane-Afrisenti at SemEval-2023 Task 12: Sentiment Analysis using Afro-centric Language Models and Adapters for Low-resource African Languages
• Authors: Israel Abebe Azime, Sana Sabah Al-Azzawi, Atnafu Lambebo Tonja, Iyanuoluwa Shode, Jesujoba Alabi, Ayodele Awokoya, Mardiyyah Oduwole, Tosin Adewumi, Samuel Fanijo, Oyinkansola Awosan, Oreen Yousuf
• Abstract summary: The task aims to perform monolingual sentiment classification (sub-task A) for 12 African languages, multilingual sentiment classification (sub-task B) and zero-shot sentiment classification (task C) Our findings suggest that using pre-trained Afro-centric language models improves performance for low-resource African languages. We also ran experiments using adapters for zero-shot tasks, and the results suggest that we can obtain promising results by using adapters with a limited amount of resources.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: AfriSenti-SemEval Shared Task 12 of SemEval-2023. The task aims to perform monolingual sentiment classification (sub-task A) for 12 African languages, multilingual sentiment classification (sub-task B), and zero-shot sentiment classification (task C). For sub-task A, we conducted experiments using classical machine learning classifiers, Afro-centric language models, and language-specific models. For task B, we fine-tuned multilingual pre-trained language models that support many of the languages in the task. For task C, we used we make use of a parameter-efficient Adapter approach that leverages monolingual texts in the target language for effective zero-shot transfer. Our findings suggest that using pre-trained Afro-centric language models improves performance for low-resource African languages. We also ran experiments using adapters for zero-shot tasks, and the results suggest that we can obtain promising results by using adapters with a limited amount of resources.

Related papers

• Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Prompts [75.33019401706188]
  Large language models (LLMs) are known to effectively perform tasks by simply observing few exemplars. We propose to assemble synthetic exemplars from a diverse set of high-resource languages to prompt the LLMs to translate from any language into English. Our unsupervised prompting method performs on par with supervised few-shot learning in LLMs of different sizes for translations between English and 13 Indic and 21 African low-resource languages.
  arXiv  Detail & Related papers  (2023-06-20T08:27:47Z)
• DN at SemEval-2023 Task 12: Low-Resource Language Text Classification via Multilingual Pretrained Language Model Fine-tuning [0.0]
  Most existing models and datasets for sentiment analysis are developed for high-resource languages, such as English and Chinese. The AfriSenti-SemEval 2023 Shared Task 12 aims to fill this gap by evaluating sentiment analysis models on low-resource African languages. We present our solution to the shared task, where we employed different multilingual XLM-R models with classification head trained on various data.
  arXiv  Detail & Related papers  (2023-05-04T07:28:45Z)
• NLNDE at SemEval-2023 Task 12: Adaptive Pretraining and Source Language Selection for Low-Resource Multilingual Sentiment Analysis [11.05909046179595]
  This paper describes our system developed for the SemEval-2023 Task 12 "Sentiment Analysis for Low-resource African languages using Twitter dataset" Our key findings are: Adapting the pretrained model to the target language and task using a small yet relevant corpus improves performance remarkably by more than 10 F1 score points. In the shared task, our system wins 8 out of 15 tracks and, in particular, performs best in the multilingual evaluation.
  arXiv  Detail & Related papers  (2023-04-28T21:02:58Z)
• HausaNLP at SemEval-2023 Task 12: Leveraging African Low Resource TweetData for Sentiment Analysis [0.0]
  We present the findings of SemEval-2023 Task 12, a shared task on sentiment analysis for low-resource African languages using Twitter dataset. Our goal is to leverage low-resource tweet data using pre-trained Afro-xlmr-large, AfriBERTa-Large, Bert-base-arabic-camelbert-da-sentiment (Arabic-camelbert), Multilingual-BERT (mBERT) and BERT models for sentiment analysis of 14 African languages.
  arXiv  Detail & Related papers  (2023-04-26T15:47:50Z)
• Efficiently Aligned Cross-Lingual Transfer Learning for Conversational Tasks using Prompt-Tuning [98.60739735409243]
  Cross-lingual transfer of language models trained on high-resource languages like English has been widely studied for many NLP tasks. We introduce XSGD for cross-lingual alignment pretraining, a parallel and large-scale multilingual conversation dataset. To facilitate aligned cross-lingual representations, we develop an efficient prompt-tuning-based method for learning alignment prompts.
  arXiv  Detail & Related papers  (2023-04-03T18:46:01Z)
• AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African Languages [0.021987601456703476]
  We present AfroLM, a multilingual language model pretrained from scratch on 23 African languages. AfroLM is pretrained on a dataset 14x smaller than existing baselines. It is able to generalize well across various domains.
  arXiv  Detail & Related papers  (2022-11-07T02:15:25Z)
• MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition [55.95128479289923]
  African languages are spoken by over a billion people, but are underrepresented in NLP research and development. We create the largest human-annotated NER dataset for 20 African languages. We show that choosing the best transfer language improves zero-shot F1 scores by an average of 14 points.
  arXiv  Detail & Related papers  (2022-10-22T08:53:14Z)
• Multilingual Language Model Adaptive Fine-Tuning: A Study on African Languages [19.067718464786463]
  We perform multilingual adaptive fine-tuning (MAFT) on 17 most-resourced African languages and three other high-resource languages widely spoken on the African continent. To further specialize the multilingual PLM, we removed vocabulary tokens from the embedding layer that corresponds to non-African writing scripts before MAFT. Our approach is competitive to applying LAFT on individual languages while requiring significantly less disk space.
  arXiv  Detail & Related papers  (2022-04-13T16:13:49Z)
• AfroMT: Pretraining Strategies and Reproducible Benchmarks for Translation of 8 African Languages [94.75849612191546]
  AfroMT is a standardized, clean, and reproducible machine translation benchmark for eight widely spoken African languages. We develop a suite of analysis tools for system diagnosis taking into account the unique properties of these languages. We demonstrate significant improvements when pretraining on 11 languages, with gains of up to 2 BLEU points over strong baselines.
  arXiv  Detail & Related papers  (2021-09-10T07:45:21Z)
• Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation [105.41167108465085]
  Cross-lingual Machine Reading (CLMRC) remains a challenging problem due to the lack of large-scale datasets in low-source languages. We propose a novel augmentation approach named Language Branch Machine Reading (LBMRC) LBMRC trains multiple machine reading comprehension (MRC) models proficient in individual language. We devise a multilingual distillation approach to amalgamate knowledge from multiple language branch models to a single model for all target languages.
  arXiv  Detail & Related papers  (2020-10-27T13:12:17Z)
• Mixed-Lingual Pre-training for Cross-lingual Summarization [54.4823498438831]
  Cross-lingual Summarization aims at producing a summary in the target language for an article in the source language. We propose a solution based on mixed-lingual pre-training that leverages both cross-lingual tasks like translation and monolingual tasks like masked language models. Our model achieves an improvement of 2.82 (English to Chinese) and 1.15 (Chinese to English) ROUGE-1 scores over state-of-the-art results.
  arXiv  Detail & Related papers  (2020-10-18T00:21:53Z)

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.