Analysis of Neural Machine Translation KANGRI Language by Unsupervised and Semi Supervised Methods

Chauhan, Shweta; Saxena, Shefali; Daniel, Philemon

doi:10.1080/03772063.2021.2016506

Cited by 7 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multi-perspective learning has also been applied in machine translation. Chauhan et al [14] designed a multi-perspective neural machine translation model that combines the source language syntactic structure perspective, the semantic feature perspective and the traditional word order perspective, which enables the translation model to better capture the multi-dimensional mapping relationship between the source language and the target language. And in the natural language generation task, utilizes a multi-perspective attention mechanism that combines the content perspective, the style perspective and the context perspective, effectively improving the quality and diversity of the generated text.…”

Section: Multi-perspective Learning In Natural Languagementioning

confidence: 99%

Migration Learning and Multi-View Training for Low-Resource Machine Translation

Yan,

Lin,

Zhao

2024

ijacsa

View full text Add to dashboard Cite

This paper discusses the main challenges and solution strategies of low-resource machine translation, and proposes a novel translation method combining migration learning and multi-view training. In a low-resource environment, neural machine translation models are prone to problems such as insufficient generalization performance, inaccurate translation of long sentences, difficulty in processing unregistered words, and inaccurate translation of domain-specific terms due to their heavy reliance on massively parallel corpora. Migration learning gradually adapts to the translation tasks of low-resource languages in the process of fine-tuning by borrowing the general translation knowledge of high-resource languages and utilizing pre-training models such as BERT, XLM-R, and so on. Multiperspective training, on the other hand, emphasizes the integration of source and target language features from multiple levels, such as word level, syntax and semantics, in order to enhance the model's comprehension and translation ability under limited data conditions. In the experiments, the study designed an experimental scheme containing pre-training model selection, multi-perspective feature construction, and migration learning and multi-perspective fusion, and compared the performance with randomly initialized Transformer model, pre-training-only model, and traditional statistical machine translation model. The experiments demonstrate that the model with multi-view training strategy significantly outperforms the baseline model in evaluation metrics such as BLEU, TER, and ChrF, and exhibits stronger robustness and accuracy in processing complex language structures and domain-specific terminology.

show abstract

Section: Multi-perspective Learning In Natural Languagementioning

confidence: 99%

Migration Learning and Multi-View Training for Low-Resource Machine Translation

Yan,

Lin,

Zhao

2024

ijacsa

View full text Add to dashboard Cite

show abstract

“…In recent years, there have been significant advancements in cross-language translation technology, particularly in the realm of English grammar [5]. These developments have been largely driven by the integration of artificial intelligence and machine learning techniques.…”

Section: Introductionmentioning

confidence: 99%

Cross-Language Translation and Comparative Study of English Literature Using Machine Translation Algorithms

Shanshan Wang

2024

jes

View full text Add to dashboard Cite

Cross-language processing in English literature involves the translation and analysis of literary texts from English into other languages or vice versa. This multidimensional task encompasses various aspects, including language translation, cultural adaptation, and literary interpretation. Through cross-language processing, literary works originally written in English can reach a wider audience, enabling individuals from diverse linguistic backgrounds to access and appreciate the richness of English literature. This paper presents an innovative approach to language processing tasks through the integration of Ant Swarm Domain Statistical Machine Learning (ASDS-ML). Leveraging principles of swarm intelligence and statistical learning techniques, ASDS-ML offers a robust framework for addressing challenges in language translation and classification. In the domain of translation, ASDS-ML demonstrates promising results in achieving accurate and nuanced translations across diverse language pairs, while also exhibiting adaptability to varying linguistic contexts. Furthermore, ASDS-ML showcases its effectiveness in text classification tasks, accurately categorizing instances across multiple classes with high precision and recall. In language translation tasks, ASDS-ML achieves an average BLEU score of 0.85 across multiple language pairs, outperforming baseline methods by 10%. Additionally, in text classification tasks, ASDS-ML achieves an average accuracy of 0.92 across ten different classes, surpassing existing approaches by 5%.

show abstract