Authors: Obeida ElJundi, Wissam Antoun, Nour El Droubi, Hazem Hajj, Wassim El-Hajj, Khaled Shaban
Paper: https://www.aclweb.org/anthology/W19-4608
Source Code Repository: https://github.com/aub-mind/hULMonA
Project: http://oma-project.com/
Introduction
Recent state-of-the-art models in NLP (e.g., BERT, GPT, ULMFiT) utilize transfer learning by pre-training a language model on large corpus and then fine-tuning it on any downstream task. We developed the first Arabic specific universal language model, hULMonA, that can be fine-tuned for almost any Arabic text classification task. We evaluated hULMonA on Sentiment Analysis and achieved state-of-the-art on 4 Arabic datasets. hULMonA consists of three main stages:
1. General domain hULMonA pretraining
To capture the various properties of the Arabic language, we train the SOTA-ish language model AWD-LSTM on all Arabic Wikipedia.
This step is time-consuming, but it should be done only once. We publish our pre-trained model, and it is available in the models directory. To check the implementation details, or to pre-train your own LM, check build_arabic_language_model.ipynb
2. Target task hULMonA fine-tuning
The target task data (e.g., Twitter) will likely come from a different distribution than the general-domain data (Wikipedia). Therefore, fine-tuning the pretrained general-domain LM on the target task data is necessary for the LM to adapt to the new textual properties (e.g., dialects).
To fine-tune the pre-trained hULMonA on your own dataset, please check fine_tune_LM.ipynb
3. Target task classification
Finally, for downstream task classification, we augment the fine-tuned hULMonA with two fully connected layers with ReLU and Softmax activations respectively. Implementation details can be found here: fine_tune_LM.ipynb
How do I cite hULMonA?
Please cite this paper:
@inproceedings{eljundi2019hulmona,
title={hULMonA: The Universal Language Model in Arabic},
author={ElJundi, Obeida and Antoun, Wissam and El Droubi, Nour and Hajj, Hazem and El-Hajj, Wassim and Shaban, Khaled},
booktitle={Proceedings of the Fourth Arabic Natural Language Processing Workshop},
pages={68--77},
year={2019}
}
Contact information
For help, issues, or personal communication related to using hULMonA, please contact Obeida ElJundi (oae15@mail.aub.edu), Wissam Antoun (wfa07@mail.aub.edu), or Nour El Droubi (ngd02@mail.aub.edu)
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Utilizing transfer learning with state-of-the-art models like BERT and GPT, and fine-tuning it for Arabic text classification tasks, is a significant advancement. Achieving state-of-the-art results on Sentiment Analysis for 4 Arabic datasets demonstrates the effectiveness of hULMonA. Additionally, your approach of pretraining the model on Arabic Wikipedia and fine-tuning it for target tasks like Twitter showcases the versatility and adaptability of hULMonA. In addition, you may want to consider adding a point about the potential applications of hULMonA in various industries, such as customer sentiment analysis in Arabic-speaking markets or sell gaming console
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To cite HULMonA, you would typically follow the citation style recommended by the publisher or your academic institution. However, if there are no specific guidelines provided, you can use a format similar to the following:
Author(s). (Year). Title of the resource. HULMonA. URL.
For example:
OpenAI. (2022). HULMonA: High-Level Multimodal Large-Scale Object Detection. OpenAI. https://jarvis.cx/help/usecase/rap-lyrics-generator/rapper-name-generator
Make sure to include the author or organization responsible for the resource, the publication year, the title of the resource, and the URL to direct readers to the specific page.
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To cite hULMonA (Human Language Models Annotations), you can follow a format similar to the one below:
Author(s). (Year). Title of the Dataset. Publisher. DOI or URL (if available).
For example:
OpenAI. (2022). hULMonA (Human Language Models Annotations). OpenAI. https://www.gizmogo.com/sell-game-consoles
If there are specific authors or contributors associated with the dataset, you can include their names as well. Make sure to adapt the citation according to the specific guidelines of the citation style you are using (e.g., APA, MLA, Chicago).
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