Hindi-Urdu Adposition and Case Supersenses v1.0

Arora, Aryaman; Venkateswaran, Nitin; Schneider, Nathan

doi:10.48550/arxiv.2103.01399

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2024

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“…They also discuss various machine translation techniques. Arora et al [12] suggested a multilingual machine translation program capable of translating into Hindi and Urdu. The system uses an artificial neural network and a translation rules-based technique.…”

Section: Related Workmentioning

confidence: 99%

Automatic translation from English to Amazigh using transformer learning

Maarouf,

El Ayachi

et al. 2024

IJEECS

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Due to the lack of parallel data, to our knowledge, no study has been conducted on the Amazigh-English language pair, despite the numerous machine translation studies completed between major European language pairs. We decided to utilize the neural machine translation (NMT) method on a parallel corpus of 137,322 sentences. The attention-based encoder-decoder architecture is used to construct statistical machine translation (SMT) models based on Moses, as well as NMT models using long short-term memory (LSTM), gated recurrent units (GRU), and transformers. Various outcomes were obtained for each strategy after several simulations: 80.7% accuracy was achieved using the statistical approach, 85.2% with the GRU model, 87.9% with the LSTM model, and 91.37% with the transformer.

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Section: Related Workmentioning

confidence: 99%