Revisiting Challenges in Neural Machine Translation

Salute the Classic: Revisiting Challenges of Machine Translation in ...

Abstract:The evolution of Neural Machine Translation (NMT) has been significantly influenced by six core challenges (Koehn and Knowles, 2017) ...

Revisiting Challenges in Neural Machine Translation. Rico Sennrich. University of Edinburgh. Rico Sennrich. Revisiting Challenges in NMT. 1 / 49. Page 2. Why ...

[PDF] Salute the Classic: Revisiting Challenges of Machine ...

This study revisits six core challenges of Neural Machine Translation, offering insights into their ongoing relevance in the context of advanced Large ...

Salute the Classic: Revisiting Challenges of Machine Translation in ...

The evolution of Neural Machine Translation (NMT) has been significantly influenced by six core challenges (Koehn and Knowles, 2017), which ...

[2401.08350] Salute the Classic: Revisiting Challenges of Machine ...

The evolution of Neural Machine Translation (NMT) has been significantly influenced by six core challenges Koehn and Knowles (2017) , which have acted as ...

Revisiting Character-Based Neural Machine Translation with ...

Translating characters instead of words or word-fragments has the potential to simplify the processing pipeline for neural machine translation (NMT), and ...

Revisiting Negation in Neural Machine Translation - MIT Press Direct

We show that the ability of neural machine translation (NMT) models to translate negation has improved with deeper and more advanced networks, ...

Machine Translation Weekly 5: Revisiting Low-Resource Neural ...

Now after 2 years, researchers from Edinburgh revisited this problem and came to an entirely different conclusion. Neural machine translation ...

Neural Machine Translation: A Review

Revisiting character- based neural machine translation with capacity and compression. ... Six challenges for neural machine translation. In Pro- ceedings of the ...

Revisiting Low-Resource Neural Machine Translation: A Case Study

Although deep neural models produce state-of-the-art results in many translation tasks, they are found to underperform phrase-based ...

Revisiting Rule-Based and Neural Machine Translation - MDPI

A series of experiments shows that the hybrid system's translation accuracy is improved, especially in out-of-domain translations, and classification accuracy ...

Revisiting Low-Resource Neural Machine Translation: A Case Study

... Instead of introducing more data, in this paper, we explore the effects of different data processing and model settings for the CCMT 2022 Chinese↔Thai low- ...

Revisiting Low-Resource Neural Machine Translation: A Case Study

Revisiting Low-Resource Neural Machine Translation: A Case Study. Rico Sennrich1,2. Biao Zhang1. 1School of Informatics, University of Edinburgh rico.sennrich ...

Rethinking the Exploitation of Monolingual Data for Low-Resource ...

The Neural Machine Translation (NMT) model has shown significant improvement in translation tasks when trained on large-scale parallel data. However, low- ...

[PDF] Revisiting Negation in Neural Machine Translation | Semantic ...

This is not a Dataset: A Large Negation Benchmark to Challenge Large Language Models · Computer Science, Linguistics. EMNLP · 2023.

Revisiting Words (Chapter 12) - Neural Machine Translation

A summary is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the 'Save PDF' ...

Revisiting Multi-Domain Machine Translation - MIT Press Direct

Massively multilingual neural machine translation in the wild: Findings and challenges. arXiv e-prints, abs/1907.05019. Amittai Axelrod, Xiaodong He, and ...

What are the current challenges in neural machine translation?

Low resources languages result in poor translation. · Difficult to generate a coherent long sentence · Content Translation Vs Context Translation ...

Revisiting Pre-training of Embedding Layers in Transformer-based ...

In this study, we focus on domain-specific dedicated neural machine translation (NMT) models, which still have the advantage in a high-resource situation as ...

Rethinking the Exploitation of Monolingual Data for Low-Resource ...

The utilization of monolingual data has been shown to be a promising strategy for addressing low-resource machine translation problems. Previous ...