Unsupervised SMT: an analysis of Indic languages and a low resource language

Saxena, Shefali; Chauhan, Shweta; Arora, Paras; Daniel, Philemon

doi:10.1080/0952813x.2022.2115142

Cited by 2 publications

(1 citation statement)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Explicit ORdering): an evaluation system that integrates a variety of factors such as exact matching, stemming matching and word order information [30].…”

Section: Meteor (Metric For Evaluation Of Translation Withmentioning

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

“…Explicit ORdering): an evaluation system that integrates a variety of factors such as exact matching, stemming matching and word order information [30].…”

Section: Meteor (Metric For Evaluation Of Translation Withmentioning

confidence: 99%