A Simple and Effective Method of Cross-Lingual Plagiarism Detection

• URL: http://arxiv.org/abs/2304.01352v2
• Date: Wed, 5 Apr 2023 09:23:17 GMT
• Title: A Simple and Effective Method of Cross-Lingual Plagiarism Detection
• Authors: Karen Avetisyan, Arthur Malajyan, Tsolak Ghukasyan, Arutyun Avetisyan
• Abstract summary: We present a simple cross-lingual plagiarism detection method applicable to a large number of languages. The presented approach leverages open multilingual thesauri for candidate retrieval task and pre-trained multilingual BERT-based language models for detailed analysis. The effectiveness of the proposed approach is demonstrated for several existing and new benchmarks, achieving state-of-the-art results for French, Russian, and Armenian languages.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a simple cross-lingual plagiarism detection method applicable to a large number of languages. The presented approach leverages open multilingual thesauri for candidate retrieval task and pre-trained multilingual BERT-based language models for detailed analysis. The method does not rely on machine translation and word sense disambiguation when in use, and therefore is suitable for a large number of languages, including under-resourced languages. The effectiveness of the proposed approach is demonstrated for several existing and new benchmarks, achieving state-of-the-art results for French, Russian, and Armenian languages.

Related papers

• Synergistic Approach for Simultaneous Optimization of Monolingual, Cross-lingual, and Multilingual Information Retrieval [5.446052898856584]
  This paper proposes a novel hybrid batch training strategy to improve zero-shot retrieval performance across monolingual, cross-lingual, and multilingual settings. The approach fine-tunes multilingual language models using a mix of monolingual and cross-lingual question-answer pair batches sampled based on dataset size.
  arXiv  Detail & Related papers  (2024-08-20T04:30:26Z)
• Understanding Cross-Lingual Alignment -- A Survey [52.572071017877704]
  Cross-lingual alignment is the meaningful similarity of representations across languages in multilingual language models. We survey the literature of techniques to improve cross-lingual alignment, providing a taxonomy of methods and summarising insights from throughout the field.
  arXiv  Detail & Related papers  (2024-04-09T11:39:53Z)
• Multilingual Few-Shot Learning via Language Model Retrieval [18.465566186549072]
  Transformer-based language models have achieved remarkable success in few-shot in-context learning. We conduct a study of retrieving semantically similar few-shot samples and using them as the context. We evaluate the proposed method on five natural language understanding datasets related to intent detection, question classification, sentiment analysis, and topic classification.
  arXiv  Detail & Related papers  (2023-06-19T14:27:21Z)
• Multilingual Representation Distillation with Contrastive Learning [20.715534360712425]
  We integrate contrastive learning into multilingual representation distillation and use it for quality estimation of parallel sentences. We validate our approach with multilingual similarity search and corpus filtering tasks.
  arXiv  Detail & Related papers  (2022-10-10T22:27:04Z)
• Meta-Learning a Cross-lingual Manifold for Semantic Parsing [75.26271012018861]
  Localizing a semantic to support new languages requires effective cross-lingual generalization. We introduce a first-order meta-learning algorithm to train a semantic annotated with maximal sample efficiency during cross-lingual transfer. Results across six languages on ATIS demonstrate that our combination of steps yields accurate semantics sampling $le$10% of source training data in each new language.
  arXiv  Detail & Related papers  (2022-09-26T10:42:17Z)
• Are Multilingual Models Effective in Code-Switching? [57.78477547424949]
  We study the effectiveness of multilingual language models to understand their capability and adaptability to the mixed-language setting. Our findings suggest that pre-trained multilingual models do not necessarily guarantee high-quality representations on code-switching.
  arXiv  Detail & Related papers  (2021-03-24T16:20:02Z)
• UNKs Everywhere: Adapting Multilingual Language Models to New Scripts [103.79021395138423]
  Massively multilingual language models such as multilingual BERT (mBERT) and XLM-R offer state-of-the-art cross-lingual transfer performance on a range of NLP tasks. Due to their limited capacity and large differences in pretraining data, there is a profound performance gap between resource-rich and resource-poor target languages. We propose novel data-efficient methods that enable quick and effective adaptation of pretrained multilingual models to such low-resource languages and unseen scripts.
  arXiv  Detail & Related papers  (2020-12-31T11:37:28Z)
• Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation [105.41167108465085]
  Cross-lingual Machine Reading (CLMRC) remains a challenging problem due to the lack of large-scale datasets in low-source languages. We propose a novel augmentation approach named Language Branch Machine Reading (LBMRC) LBMRC trains multiple machine reading comprehension (MRC) models proficient in individual language. We devise a multilingual distillation approach to amalgamate knowledge from multiple language branch models to a single model for all target languages.
  arXiv  Detail & Related papers  (2020-10-27T13:12:17Z)
• Multilingual Chart-based Constituency Parse Extraction from Pre-trained Language Models [21.2879567125422]
  We propose a novel method for extracting complete (binary) parses from pre-trained language models. By applying our method on multilingual PLMs, it becomes possible to induce non-trivial parses for sentences from nine languages.
  arXiv  Detail & Related papers  (2020-04-08T05:42:26Z)
• XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization [128.37244072182506]
  Cross-lingual TRansfer Evaluation of Multilinguals XTREME is a benchmark for evaluating the cross-lingual generalization capabilities of multilingual representations across 40 languages and 9 tasks. We demonstrate that while models tested on English reach human performance on many tasks, there is still a sizable gap in the performance of cross-lingually transferred models.
  arXiv  Detail & Related papers  (2020-03-24T19:09:37Z)

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.