Syntax-based data augmentation for Hungarian-English machine translation

Nagy, Attila; Patrick, Nanys,; Konrád, Balázs Frey; Bial, Bence; Ács, Judit

doi:10.48550/arxiv.2201.06876

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…Automated evaluation metrics BLEU is used to assess the quality of the translations. 60 The similarity scores employed for retrieving match sentences are edit distance, fuzzy matching score, n-gram matching score, sentence embedding score. BLEU is measured for these NFR methods.…”

Section: Validation With Dataset Imentioning

confidence: 99%

Novel algorithm machine translation for language translation tool

Jayasakthi Velmurugan,

Sumathy,

Pradeep

2024

Computational Intelligence

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Fuzzy matching techniques are the presently used methods in translating the words. Neural machine translation and statistical machine translation are the methods used in MT. In machine translator tool, the strategy employed for translation needs to handle large amount of datasets and therefore the performance in retrieving correct matching output can be affected. In order to improve the matching score of MT, the advanced techniques can be presented by modifying the existing fuzzy based translator and neural machine translator. The conventional process of modifying architectures and encoding schemes are tedious process. Similarly, the preprocessing of datasets also involves more time consumption and memory utilization. In this article, a new spider web based searching enhanced translation is presented to be employed with the neural machine translator. The proposed scheme enables deep searching of available dataset to detect the accurate matching result. In addition, the quality of translation is improved by presenting an optimal selection scheme for using the sentence matches in source augmentation. The matches retrieved using various matching scores are applied to an optimization algorithm. The source augmentation using optimal retrieved matches increases the translation quality. Further, the selection of optimal match combination helps to reduce time requirement, since it is not necessary to test all retrieved matches in finding target sentence. The performance of translation is validated by measuring the quality of translation using BLEU and METEOR scores. These two scores can be achieved for the TA‐EN language pairs in different configurations of about 92% and 86%, correspondingly. The results are evaluated and compared with other available NMT methods to validate the work.

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Section: Validation With Dataset Imentioning

confidence: 99%

Novel algorithm machine translation for language translation tool

Jayasakthi Velmurugan,

Sumathy,

Pradeep

2024

Computational Intelligence

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“…Syntactically aware models have also effectively solved various NLP problems, i.e., sentiment analysis, text generation [88], [89], question answering [90], and semantic role labeling [90], etc. In particular, dependency trees are used as syntactic information in problems such as machine translation [91], [92], language model [93], [94], and semantic 1 https://sentic.net/ Last visited: 04-05-2023 role labeling [95]. Bastings et al [91] presented Graph Convolutional Network (GCN) using a dependency tree for machine translation.…”

Section: Literature Reviewmentioning

confidence: 99%

Emotion Classification in Texts Over Graph Neural Networks: Semantic Representation is Better Than Syntactic

Ameer

Bölücü

Sidorov³

et al. 2023

IEEE Access

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Social media is a widely used platform that provides a huge amount of user-generated content that can be processed to extract information about users' emotions. This has numerous benefits, such as understanding how individuals feel about certain news or events. It can be challenging to categorize emotions from text created on social media, especially when trying to identify several different emotions from a short text length, as in a multi-label classification problem. Most previous work on emotion classification has focused on deep neural networks such as Convolutional Neural Networks and Recurrent Neural Networks. However, none of these networks have used semantic and syntactic knowledge to classify multiple emotions from a text. In this study, semantic and syntactic aware graph attention networks were proposed to classify emotions from a text with multiple labels. We integrated semantic information in the graph attention network in the form of Universal Conceptual Cognitive Annotation and syntactic information in the form of dependency trees. Our extensive experimental results showed that our two models, UCCA-GAT (accuracy = 71.2) and Dep-GAT (accuracy = 68.7), were able to outperform the state-of-the-art performance on both the challenging SemEval-2018 E-c: Detecting Emotions (multi-label classification) English dataset (accuracy = 58.8) and GoEmotions dataset (accuracy = 65.9).

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Syntax-based data augmentation for Hungarian-English machine translation

Cited by 2 publications

References 18 publications

Novel algorithm machine translation for language translation tool

Novel algorithm machine translation for language translation tool

Emotion Classification in Texts Over Graph Neural Networks: Semantic Representation is Better Than Syntactic

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