Investigating Monolingual and Multilingual BERT Models for Vietnamese Aspect Category Detection

Thin, Dang Van; Le, Lac Si; Hoang, Vu Xuan; Nguyen, Ngan Luu-Thuy

doi:10.1109/rivf55975.2022.10013792

Cited by 7 publications

(3 citation statements)

References 17 publications

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“…Liao et al [20] used RoBERTa (robustly optimized BERT pre-training approach) for contextual feature representation and combined it with 1D-CNN and cross-attention for aspect category classification. In [21], the authors performed different pre-trained language models (monolingual and multilingual) on the Vietnamese language.…”

Section: Related Workmentioning

confidence: 99%

MTL-AraBERT: An Enhanced Multi-Task Learning Model for Arabic Aspect-Based Sentiment Analysis

Fadel,

Saleh,

Salama

et al. 2024

Computers

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Aspect-based sentiment analysis (ABSA) is a fine-grained type of sentiment analysis; it works on an aspect level. It mainly focuses on extracting aspect terms from text or reviews, categorizing the aspect terms, and classifying the sentiment polarities toward each aspect term and aspect category. Aspect term extraction (ATE) and aspect category detection (ACD) are interdependent and closely associated tasks. However, the majority of the current literature on Arabic aspect-based sentiment analysis (ABSA) deals with these tasks individually, assumes that aspect terms are already identified, or employs a pipeline model. Pipeline solutions employ single models for each task, where the output of the ATE model is utilized as the input for the ACD model. This sequential process can lead to the propagation of errors across different stages, as the performance of the ACD model is influenced by any errors produced by the ATE model. Therefore, the primary objective of this study was to investigate a multi-task learning approach based on transfer learning and transformers. We propose a multi-task learning model (MTL) that utilizes the pre-trained language model (AraBERT), namely, the MTL-AraBERT model, for extracting Arabic aspect terms and aspect categories simultaneously. Specifically, we focused on training a single model that simultaneously and jointly addressed both subtasks. Moreover, this paper also proposes a model integrating AraBERT, single pair classification, and BiLSTM/BiGRU that can be applied to aspect term polarity classification (APC) and aspect category polarity classification (ACPC). All proposed models were evaluated using the SemEval-2016 annotated dataset for the Arabic hotel dataset. The experiment results of the MTL model demonstrate that the proposed models achieved comparable or better performance than state-of-the-art works (F1-scores of 80.32% for the ATE and 68.21% for the ACD). The proposed SPC-BERT model demonstrated high accuracy, reaching 89.02% and 89.36 for APC and ACPC, respectively. These improvements hold significant potential for future research in Arabic ABSA.

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Section: Related Workmentioning

confidence: 99%

MTL-AraBERT: An Enhanced Multi-Task Learning Model for Arabic Aspect-Based Sentiment Analysis

Fadel,

Saleh,

Salama

et al. 2024

Computers

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“…The amount of Vietnamese data makes up the majority of the dataset. For Vietnamese language, XLM-R is the best multilingual model based on the previous research [28].…”

Section: Approachmentioning

confidence: 99%

vnNLI - VLSP 2021: Vietnamese and English-Vietnamese Textual Entailment Based on Pre-trained Multilingual Language Models

Thuy

Thìn²,

Vũ³

et al. 2022

JCSCE

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Natural Language Inference (NLI) is a high-level semantic task in Natural Language Processing - NLP, and it extends further challenges if it is in the cross-lingual scenario. In recent years, pre-trained multilingual language models (e.g., mBERT ,XLM-R, InfoXLM) have greatly contributed to the success of dealing with these challenges. Based on the motivation behind these achievements, this paper describes our approach based on fine-tuning pretrained multilingual language models (XLM-R, InfoXLM) to tackle the shared task ``Vietnamese and English\-Vietnamese Textual Entailment'' at the 8th International Workshop on Vietnamese Language and Speech Processing (VLSP 2021\footnote{https://vlsp.org.vn/vlsp2021}). We investigate other techniques to improve the performance of our work: Cross-validation, Pseudo-labeling (PL), Learning rate adjustment, and Postagging. All experimental results demonstrated that our approach based on the InfoXLM model achieved competitive results, ranking 2nd for the task evaluation in VLSP 2021 with 0.89 in terms of F1-score on the private test set.

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“…Multilingual model: We chose XLM-R over mT5 [21] and mBERT [22] because XLM-R generally performs better than mT5, mBERT at the same model size (see original paper for details). The work of [23] demonstrated that the XLM-R model is currently the best multilingual model for Vietnamese language.…”

Section: Classifier Architecturementioning

confidence: 99%

vnNLI - VLSP2021: An Empirical Study on Vietnamese-English Natural Language Inference Based on Pretrained Language Models with Data Augmentation

Van

Hao²,

Nguyễn³

et al. 2022

JCSCE

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In this paper, we describe an empirical study of data augmentation techniques with various pre-trained language models on the bilingual dataset which was presented at the VLSP 2021 - Vietnamese and English-Vietnamese Textual Entailment. We apply the machine translation tool to generate new training set from original training data and then investigate and compare the effectiveness of a monolingual and multilingual model on the new data set. Our experimental results show that fine-tuning a pre-trained multilingual language XLM-R model with an augmented training set gives the best performance. Our system was ranked third in the shared-task VLSP 2021 with the F1-score of about 0.88.

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Investigating Monolingual and Multilingual BERT Models for Vietnamese Aspect Category Detection

Cited by 7 publications

References 17 publications

MTL-AraBERT: An Enhanced Multi-Task Learning Model for Arabic Aspect-Based Sentiment Analysis

MTL-AraBERT: An Enhanced Multi-Task Learning Model for Arabic Aspect-Based Sentiment Analysis

vnNLI - VLSP 2021: Vietnamese and English-Vietnamese Textual Entailment Based on Pre-trained Multilingual Language Models

vnNLI - VLSP2021: An Empirical Study on Vietnamese-English Natural Language Inference Based on Pretrained Language Models with Data Augmentation

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