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Knowledge-Enhanced Vietnamese Paraphrase Identification

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
Minh Lu Xuan
Minh Nguyen Hong
Loc Nguyen Xuan
Thai Do Thanh
Duc Bui Tien
Quang Tran Minh
Published in
Multi-disciplinary Trends in Artificial Intelligence
Publisher
Springer Nature Singapore

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Abstract

This chapter delves into the complexities of Vietnamese Paraphrase Identification (PI), a crucial task for various natural language processing applications. The study highlights the unique challenges posed by the Vietnamese language, such as its syllable-based orthography and six-tone system, which complicate word segmentation and disambiguation. The authors propose a method that enhances pretrained language models (PLMs) with external knowledge, specifically by incorporating named entity information from Wikipedia using Wikipedia2Vec. The experimental results demonstrate significant improvements in PI accuracy, outperforming strong baselines like BERT and its variants. The chapter also discusses the corpora used, including the vnPara corpus and an augmented version enriched with diverse entities. The detailed evaluation of different PLMs and the comprehensive analysis of the results provide valuable insights into the effectiveness of knowledge-enhanced models for Vietnamese PI. This study is a significant contribution to the field, offering practical solutions and theoretical advancements that can be applied to other languages and NLP tasks.
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previous chapter Efficient Learning of Horn Formulas over Finite Totally Ordered Domains
next chapter Co-NAML-LSTUR: A Combined Model with Attentive Multi-view Learning and Long-and Short-Term User Representations for News Recommendation
Title
Knowledge-Enhanced Vietnamese Paraphrase Identification
Authors
Minh Lu Xuan
Minh Nguyen Hong
Loc Nguyen Xuan
Thai Do Thanh
Duc Bui Tien
Quang Tran Minh
Copyright Year
2026
Publisher
Springer Nature Singapore
Book
Multi-disciplinary Trends in Artificial Intelligence

Print ISBN: 978-981-9549-59-7

Electronic ISBN: 978-981-9549-60-3

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-981-95-4960-3_8
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