LSTM Easy-first Dependency Parsing with Pre-trained Word Embeddings and Character-level Word Embeddings in Vietnamese

Nguyen, B.D.; Nguyen, Kiet Van; Nguyen, Ngan Luu-Thuy

doi:10.1109/kse.2018.8573397

Cited by 4 publications

(3 citation statements)

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“…Regarding to the Vietnamese language processing, there are quite a number of research works on other tasks such as parsing [50]- [52], part-of-speech [53], [54], named entity recognition [55]- [57], sentiment analysis [58]- [60], and question answering [61]- [63]. However, to the extent of our knowledge, there are no research publications on multiplechoice machine reading comprehension.…”

Section: B Mrc Methodsmentioning

confidence: 99%

Enhancing Lexical-Based Approach With External Knowledge for Vietnamese Multiple-Choice Machine Reading Comprehension

et al. 2020

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Although Vietnamese is the 17 th most popular native-speaker language a in the world, there are not many research studies on Vietnamese machine reading comprehension (MRC), the task of understanding a text and answering questions about it. One of the reasons is because of the lack of high-quality benchmark datasets for this task. In this work, we construct a dataset which consists of 2,783 pairs of multiple-choice questions and answers based on 417 Vietnamese texts which are commonly used for teaching reading comprehension for elementary school pupils. In addition, we propose a lexicalbased MRC method that utilizes semantic similarity measures and external knowledge sources to analyze questions and extract answers from the given text. We compare the performance of the proposed model with several baseline lexical-based and neural network-based models. Our proposed method achieves 61.81% by accuracy, which is 5.51% higher than the best baseline model. We also measure human performance on our dataset and find that there is a big gap between machine-model and human performances. This indicates that significant progress can be made on this task. The dataset is freely available on our website b for research purposes.

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Section: B Mrc Methodsmentioning

confidence: 99%

Enhancing Lexical-Based Approach With External Knowledge for Vietnamese Multiple-Choice Machine Reading Comprehension

et al. 2020

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“…Although researchers have studied several works on the Vietnamese language, such as parsing (Nguyen et al, 2009b(Nguyen et al, , 2014bNguyen et al, 2018a), part-of-speech (Nguyen et al, 2014a;Bach et al, 2018), named entity recognition (Thao et al, 2008;Nguyen et al, , 2019, sentiment analysis (Nguyen et al, 2018b,c;Dang et al, 2018), and question answering (Nguyen et al, 2009a;Nguyen and Le, 2016;Le and Bui, 2018), there is only one corpus for MRC (Nguyen et al, 2020). This corpus comprises of 2,783 multiple-choice questions and answers based on a set of 417 Vietnamese texts used for teaching reading comprehension to 1 st to 5 th graders.…”

Section: Introductionmentioning

confidence: 99%

New Vietnamese Corpus for Machine Reading Comprehension of Health News Articles

Nguyen¹,

Huynh²,

Nguyen³

et al. 2020

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Although over 95 million people in the world speak the Vietnamese language, there are not any large and qualified datasets for automatic reading comprehension. In addition, machine reading comprehension for the health domain offers great potential for practical applications; however, there is still very little machine reading comprehension research in this domain. In this study, we present ViNewsQA as a new corpus for the low-resource Vietnamese language to evaluate models of machine reading comprehension. The corpus comprises 10,138 human-generated question-answer pairs. Crowdworkers created the questions and answers based on a set of over 2,030 online Vietnamese news articles from the VnExpress news website, where the answers comprised spans extracted from the corresponding articles. In particular, we developed a process of creating a corpus for the Vietnamese language. Comprehensive evaluations demonstrated that our corpus requires abilities beyond simple reasoning such as word matching, as well as demanding difficult reasoning similar to inferences based on single-or-multiple-sentence information. We conducted experiments using state-of-the-art methods for machine reading comprehension to obtain the first baseline performance measures, which will be compared with further models' performances. We measured human performance based on the corpus and compared it with several strong neural models. Our experiments showed that the best model was BERT, which

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“…Parsing is a descriptive linguistics exercise that involves breaking down the text into elements of speech with evidence of the form, function, and syntax of the relationship of each part so that the text can be well understood (ThoughtCo., 2019). Easy-first parsing is a transition based parsing which is a type of syntactic parsing algorithm (Nguyen et al, 2018). The Easy-First parsing algorithm performs work based on the difficulty level of the task.…”

Section: Introductionmentioning

confidence: 99%

Dependency Parsing for Arabic Quran using Easy-First Parsing Algorithm

2020

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Arabic is the main language of Al-Quran. Nowadays, many people are studying the Language of Al-Quran, called Quran Arabic. For the beginners, it is important for them to understand the syntactic relationship in a sentence found in the Qur'an. If they do not understand enough, the interpretation will be different and wrong. It will turn into dangerous because Al-Quran is a source of guidance for Muslims’ life. Dependency parsing is very important for linguistic research, especially for rich languages such as the Arabic Language. This study aims to build dependency parsing, in order to make it easier to get to understand syntactic relationship information in sentences. This study uses a parsing method called deterministic parsing, which the method used is shift-reduce parsing with the Easy-First parsing algorithm. The evaluation used labeled attachment score calculation. The score generated from the evaluation was 69.7, beforehand, the comparison both the system results and the gold standard have been done. 62 sentences found the correct head and relation in each word. The number of words found to be wrong is not more than 3 words in one sentence. Evaluation scores produced are not exorbitant due to the complicated tagset used and lacking test sentences.

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LSTM Easy-first Dependency Parsing with Pre-trained Word Embeddings and Character-level Word Embeddings in Vietnamese

Cited by 4 publications

References 19 publications

Enhancing Lexical-Based Approach With External Knowledge for Vietnamese Multiple-Choice Machine Reading Comprehension

Enhancing Lexical-Based Approach With External Knowledge for Vietnamese Multiple-Choice Machine Reading Comprehension

New Vietnamese Corpus for Machine Reading Comprehension of Health News Articles

Dependency Parsing for Arabic Quran using Easy-First Parsing Algorithm

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