BERT Fine-tuning For Arabic Text Summarization
- URL: http://arxiv.org/abs/2004.14135v1
- Date: Sun, 29 Mar 2020 20:23:14 GMT
- Title: BERT Fine-tuning For Arabic Text Summarization
- Authors: Khalid N. Elmadani, Mukhtar Elgezouli, Anas Showk
- Abstract summary: Our model works with multilingual BERT (as Arabic language does not have a pretrained BERT of its own)
We show its performance in English corpus first before applying it to Arabic corpora in both extractive and abstractive tasks.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Fine-tuning a pretrained BERT model is the state of the art method for
extractive/abstractive text summarization, in this paper we showcase how this
fine-tuning method can be applied to the Arabic language to both construct the
first documented model for abstractive Arabic text summarization and show its
performance in Arabic extractive summarization. Our model works with
multilingual BERT (as Arabic language does not have a pretrained BERT of its
own). We show its performance in English corpus first before applying it to
Arabic corpora in both extractive and abstractive tasks.
Related papers
- Bilingual Adaptation of Monolingual Foundation Models [48.859227944759986]
We present an efficient method for adapting a monolingual Large Language Model (LLM) to another language.
Our two-stage approach begins with expanding the vocabulary and training only the embeddings matrix.
By continually pre-training on a mix of Arabic and English corpora, the model retains its proficiency in English while acquiring capabilities in Arabic.
arXiv Detail & Related papers (2024-07-13T21:09:38Z) - On the importance of Data Scale in Pretraining Arabic Language Models [46.431706010614334]
We conduct a comprehensive study on the role of data in Arabic Pretrained Language Models (PLMs)
We reassess the performance of a suite of state-of-the-art Arabic PLMs by retraining them on massive-scale, high-quality Arabic corpora.
Our analysis strongly suggests that pretraining data by far is the primary contributor to performance, surpassing other factors.
arXiv Detail & Related papers (2024-01-15T15:11:15Z) - AceGPT, Localizing Large Language Models in Arabic [73.39989503874634]
The paper proposes a comprehensive solution that includes pre-training with Arabic texts, Supervised Fine-Tuning (SFT) utilizing native Arabic instructions, and GPT-4 responses in Arabic.
The goal is to cultivate culturally cognizant and value-aligned Arabic LLMs capable of accommodating the diverse, application-specific needs of Arabic-speaking communities.
arXiv Detail & Related papers (2023-09-21T13:20:13Z) - Cross-Lingual NER for Financial Transaction Data in Low-Resource
Languages [70.25418443146435]
We propose an efficient modeling framework for cross-lingual named entity recognition in semi-structured text data.
We employ two independent datasets of SMSs in English and Arabic, each carrying semi-structured banking transaction information.
With access to only 30 labeled samples, our model can generalize the recognition of merchants, amounts, and other fields from English to Arabic.
arXiv Detail & Related papers (2023-07-16T00:45:42Z) - AraBART: a Pretrained Arabic Sequence-to-Sequence Model for Abstractive
Summarization [23.540743628126837]
We propose AraBART, the first Arabic model in which the encoder and the decoder are pretrained end-to-end, based on BART.
We show that AraBART achieves the best performance on multiple abstractive summarization datasets.
arXiv Detail & Related papers (2022-03-21T13:11:41Z) - Supporting Undotted Arabic with Pre-trained Language Models [0.0]
We study the effect of applying pre-trained Arabic language models on "undotted" Arabic texts.
We suggest several ways of supporting undotted texts with pre-trained models, without additional training, and measure their performance on two Arabic natural-language-processing tasks.
arXiv Detail & Related papers (2021-11-18T16:47:56Z) - Arabic aspect based sentiment analysis using BERT [0.0]
This article explores the modeling capabilities of contextual embeddings from pre-trained language models, such as BERT.
We are building a simple but effective BERT-based neural baseline to handle this task.
Our BERT architecture with a simple linear classification layer surpassed the state-of-the-art works, according to the experimental results.
arXiv Detail & Related papers (2021-07-28T11:34:00Z) - AraELECTRA: Pre-Training Text Discriminators for Arabic Language
Understanding [0.0]
We develop an Arabic language representation model, which we name AraELECTRA.
Our model is pretrained using the replaced token detection objective on large Arabic text corpora.
We show that AraELECTRA outperforms current state-of-the-art Arabic language representation models, given the same pretraining data and with even a smaller model size.
arXiv Detail & Related papers (2020-12-31T09:35:39Z) - Understanding Pre-trained BERT for Aspect-based Sentiment Analysis [71.40586258509394]
This paper analyzes the pre-trained hidden representations learned from reviews on BERT for tasks in aspect-based sentiment analysis (ABSA)
It is not clear how the general proxy task of (masked) language model trained on unlabeled corpus without annotations of aspects or opinions can provide important features for downstream tasks in ABSA.
arXiv Detail & Related papers (2020-10-31T02:21:43Z) - SLM: Learning a Discourse Language Representation with Sentence
Unshuffling [53.42814722621715]
We introduce Sentence-level Language Modeling, a new pre-training objective for learning a discourse language representation.
We show that this feature of our model improves the performance of the original BERT by large margins.
arXiv Detail & Related papers (2020-10-30T13:33:41Z) - AraBERT: Transformer-based Model for Arabic Language Understanding [0.0]
We pre-trained BERT specifically for the Arabic language in the pursuit of achieving the same success that BERT did for the English language.
The results showed that the newly developed AraBERT achieved state-of-the-art performance on most tested Arabic NLP tasks.
arXiv Detail & Related papers (2020-02-28T22:59:24Z)
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.