Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Employing a Multilingual Transformer Model for Segmenting Unpunctuated Arabic Text

Version 1 : Received: 25 August 2022 / Approved: 26 August 2022 / Online: 26 August 2022 (05:19:39 CEST)

A peer-reviewed article of this Preprint also exists.

Alshanqiti, A.M.; Albouq, S.; Alkhodre, A.B.; Namoun, A.; Nabil, E. Employing a Multilingual Transformer Model for Segmenting Unpunctuated Arabic Text. Appl. Sci. 2022, 12, 10559. Alshanqiti, A.M.; Albouq, S.; Alkhodre, A.B.; Namoun, A.; Nabil, E. Employing a Multilingual Transformer Model for Segmenting Unpunctuated Arabic Text. Appl. Sci. 2022, 12, 10559.

Abstract

Long unpunctuated texts containing complex linguistic sentences are a stumbling block to processing any low-resource languages. Thus, approaches that attempt to segment lengthy texts with no proper punctuation into simple candidate sentences are a vitally important preprocessing task in many hard-to-solve NLP applications. In this paper, we propose (PDTS) a punctuation detection approach for segmenting Arabic text, built on top of a multilingual BERT-based model and some generic linguistic rules. Furthermore, we showcase how PDTS can be effectively employed as a text tokenizer for unpunctuated documents (i.e., mimicking the transcribed audio-to-text documents). Experimental findings across two evaluation protocols (involving an ablation study and a human-based judgment) demonstrate that PDTS is practically effective in both performance quality and computational cost.

Keywords

text splitting; text tokenization; transfer learning; mask-fill prediction; NLP linguistic rules; missing punctuations; cross-lingual BERT model; Masked Language Modeling

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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