Multi-task Learning for Aspect and Polarity Recognition on Vietnamese Datasets

Thin, Dang Van; Nguyen, Duc-Vu; Nguyen, Kiet Van; Nguyen, Ngan Luu-Thuy; Nguyen, Anh H. T.

doi:10.1007/978-981-15-6168-9_15

Cited by 7 publications

(3 citation statements)

References 14 publications

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“…In [53], the researchers introduced a multi-task model using BiLSTM with a self-attention mechanism for the ATE task and a CNN for the APC task. In addition, in [54], the authors proposed a multitask model based on deep learning for ATE and APC tasks on the Vietnamese dataset for restaurant and hotel domains. Another study [20] employed a fine-tuned BERT model as a multi-task model for ABSA with a self-attention layer on top of the BERT model.…”

Section: Multi-task Learning For Absamentioning

confidence: 99%

Multi-Task Learning Model with Data Augmentation for Arabic Aspect-Based Sentiment Analysis

Fadel¹,

Abulnaja²,

Saleh³

2023

Computers, Materials &Amp; Continua

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Aspect-based sentiment analysis (ABSA) is a fine-grained process. Its fundamental subtasks are aspect term extraction (ATE) and aspect polarity classification (APC), and these subtasks are dependent and closely related. However, most existing works on Arabic ABSA content separately address them, assume that aspect terms are preidentified, or use a pipeline model. Pipeline solutions design different models for each task, and the output from the ATE model is used as the input to the APC model, which may result in error propagation among different steps because APC is affected by ATE error. These methods are impractical for real-world scenarios where the ATE task is the base task for APC, and its result impacts the accuracy of APC. Thus, in this study, we focused on a multi-task learning model for Arabic ATE and APC in which the model is jointly trained on two subtasks simultaneously in a single model. This paper integrates the multi-task model, namely Local Cotext Foucse-Aspect Term Extraction and Polarity classification (LCF-ATEPC) and Arabic Bidirectional Encoder Representation from Transformers (AraBERT) as a shred layer for Arabic contextual text representation. The LCF-ATEPC model is based on a multi-head selfattention and local context focus mechanism (LCF) to capture the interactive information between an aspect and its context. Moreover, data augmentation techniques are proposed based on state-of-the-art augmentation techniques (word embedding substitution with constraints and contextual embedding (AraBERT)) to increase the diversity of the training dataset. This paper examined the effect of data augmentation on the multi-task model for Arabic ABSA. Extensive experiments were conducted on the original and combined datasets (merging the original and augmented datasets). Experimental results demonstrate that the proposed Multi-task model outperformed existing APC techniques. Superior results were obtained by AraBERT and LCF-ATEPC with fusion layer (AR-LCF-ATEPC-Fusion) and the proposed data augmentation word embedding-based method (FastText) on the combined dataset.

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Section: Multi-task Learning For Absamentioning

confidence: 99%

Multi-Task Learning Model with Data Augmentation for Arabic Aspect-Based Sentiment Analysis

Fadel¹,

Abulnaja²,

Saleh³

2023

Computers, Materials &Amp; Continua

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“…In this section, we present the hyper-parameters of model and information of various pre-trained BERT models which are used in this paper. For pre-processing component, we re-implemented these steps as research works [27], [28] on Vietnamese ABSA. In addition, each pre-trained model has the specific pre-processing steps for the text data, therefore, we read and add carefully these pre-processing steps on the text input.…”

Section: B Experiments Settingsmentioning

confidence: 99%

Investigating Monolingual and Multilingual BERTModels for Vietnamese Aspect Category Detection

Thin¹,

Le²,

Hoang³

et al. 2021

Preprint

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Aspect category detection (ACD) is one of the challenging tasks in the Aspect-based sentiment Analysis problem. The purpose of this task is to identify the aspect categories mentioned in user-generated reviews from a set of pre-defined categories. In this paper, we investigate the performance of various monolingual pre-trained language models compared with multilingual models on the Vietnamese aspect category detection problem. We conduct the experiments on two benchmark datasets for the restaurant and hotel domain. The experimental results demonstrated the effectiveness of the monolingual PhoBERT model than others on two datasets. We also evaluate the performance of the multilingual model based on the combination of whole SemEval-2016 datasets in other languages with the Vietnamese dataset. To the best of our knowledge, our research study is the first attempt at performing various available pretrained language models on aspect category detection task and utilize the datasets from other languages based on multilingual models.

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“…Moreover, we can survey two well-known Vietnamese word segmenters, namely pyvi 1 and underthesea 2 scientifically unpublished up to now. For instances, the pyvi toolkit was used in the research of Van Thin et al [37] about sentiment analysis on VLSP2018-SA corpus [24] and research of Nguyen et al [38] on product reviews. Another instance, Nguyen et al [39] used underthesea toolkit for preprocessing their electronic products comments dataset.…”

Section: Introductionmentioning

confidence: 99%

Is word segmentation necessary for Vietnamese sentiment classification?

Nguyen¹,

Nguyen²

2022

2022 RIVF International Conference on Computing and Communication Technologies (RIVF)

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To the best of our knowledge, this paper made the first attempt to answer whether word segmentation is necessary for Vietnamese sentiment classification. To do this, we presented five pre-trained monolingual S4-based language models for Vietnamese, including one model without word segmentation, and four models using RDRsegmenter, uitnlp, pyvi, or underthesea toolkits in the pre-processing data phase. According to comprehensive experimental results on two corpora, including the VLSP2016-SA corpus of technical article reviews from the news and social media and the UIT-VSFC corpus of the educational survey, we have two suggestions. Firstly, using traditional classifiers like Naive Bayes or Support Vector Machines, word segmentation maybe not be necessary for the Vietnamese sentiment classification corpus, which comes from the social domain. Secondly, word segmentation is necessary for Vietnamese sentiment classification when word segmentation is used before using the BPE method and feeding into the deep learning model. In this way, the RDRsegmenter is the stable toolkit for word segmentation among the uitnlp, pyvi, and underthesea toolkits.

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Multi-task Learning for Aspect and Polarity Recognition on Vietnamese Datasets

Cited by 7 publications

References 14 publications

Multi-Task Learning Model with Data Augmentation for Arabic Aspect-Based Sentiment Analysis

Multi-Task Learning Model with Data Augmentation for Arabic Aspect-Based Sentiment Analysis

Investigating Monolingual and Multilingual BERTModels for Vietnamese Aspect Category Detection

Is word segmentation necessary for Vietnamese sentiment classification?

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