Enhancing Arabic‐text feature extraction utilizing label‐semantic augmentation in few/zero‐shot learning

Author:

Basabain Seham12ORCID,Cambria Erik3ORCID,Alomar Khalid1,Hussain Amir2

Affiliation:

1. Faculty of Computing and Information Technology King AbdulAziz University Jeddah Saudi Arabia

2. School of Computing Edinburgh Napier University Edinburgh UK

3. School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

Abstract

AbstractA growing amount of research use pre‐trained language models to address few/zero‐shot text classification problems. Most of these studies neglect the semantic information hidden implicitly beneath the natural language names of class labels and develop a meta learner from the input texts solely. In this work, we demonstrate how label information can be utilized to extract enhanced feature representation of the input text from a Transformer‐based pre‐trained language model such as AraBERT. In addition, how this approach can improve performance when the data resources are scarce like in the Arabic language and the input text is short with little semantic information as is the case using tweets. The work also applies zero‐shot text classification to predict new classes with no training examples across different domains including sarcasm detection and sentiment analysis using the information in the last layer of a trained classifier in a transfer learning setting. Experiments show that our approach has a better performance for the few‐shot sentiment classification compared to baseline models and models trained without augmenting label information. Moreover, the zero‐shot implementation achieved an accuracy up to 0.874 in Arabic sarcasm detection from a model trained on a sentiment analysis task.

Publisher

Wiley

Subject

Artificial Intelligence,Computational Theory and Mathematics,Theoretical Computer Science,Control and Systems Engineering

Reference43 articles.

1. A comparative study of effective approaches for Arabic sentiment analysis

2. Arabic question answering system: a survey

3. Antoniou A. Storkey A. &Edwards H.(2017).Data augmentation generative adversarial networks.ArXiv Preprint. ArXiv:1711.04340.

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