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  1. 3 days ago · The recent advent of neural machine translation (NMT) has pushed translation technologies to new frontiers, but its benefits are unevenly distributed 1. The vast majority of improvements made have ...

  2. May 13, 2024 · Neural machine translation (NMT) is an automatic task of translating a sequence of words from one language to another. In recent years, the development of attention-based transformer models has had a…

  3. May 29, 2024 · Neural Machine Translation (NMT) has made remarkable progress over the past years. However, under-translation and over-translation remain two challenging problems in state-of-the-art NMT systems. In this work, we conduct an in-depth analysis on the underlying cause of under-translation in NMT, providing an explanation from the perspective of ...

  4. 3 days ago · By Jourik Ciesielski Prior to the introduction of LLMs, NMT defined the computer-assisted translator’s toolset. And to some degree, it still does. But many in the industry have recently taken steps to promote LLMs to the new default. In this article, leaders from six of the world’s most influential language companies share their perspectives on the best approach to automated ...

  5. May 16, 2024 · Neural Machine Translation (NMT) has seen tremendous growth in the last ten years since the early 2000s and has already entered a mature phase. While considered the most widely used solution for Machine Translation, its performance on low-resource ...

  6. May 14, 2024 · In this paper, we present the first translation tool for Owens Valley Paiute (a critically endangered Indigenous American language) and, in doing so, propose a new methodology for low/no-resource machine translation: LLM-RBMT (LLM-Assisted Rule-Based Machine Translation).

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  8. 3 days ago · Neural machine translation (NMT) is one such task that LLMs have been applied to with great success. However, little research has focused on applying LLMs to the more difficult subset of NMT called simultaneous translation (SimulMT), where translation begins before the entire source context is available to the model.

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