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Introduction Neural Machine Translation has gained much momentum these days. It has greatly improved from the traditional statistical machine translation, and achieved state-of-the-art performance on ...
Overview Machine translation using a sequence-to-sequence (seq2seq) encoder-decoder model has revolutionized the field of language translation. By leveraging the power of deep learning and recurrent ...
The main purpose of multimodal machine translation (MMT) is to improve the quality of translation results by taking the corresponding visual context as an additional input. Recently many studies in ...
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Recent research sheds light on the strengths and weaknesses of encoder-decoder and decoder-only models architectures in machine translation tasks.
Encoder-decoder frameworks are widely used in natural language processing and speech processing, such as in end-to-end neural machine translation models and speech recognition models.
The main purpose of multimodal machine translation is to improve the quality of translation results by taking the corresponding visual context as an additional input. Recently many studies in neural ...
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