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Neural machine translation (NMT) is an approach to machine translation that uses an artificial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model.
Jun 3, 2019 · 21 min read. ·. Jun 3, 2019. -- 5. Image from pixabay.com. Machine Translation (MT) is a subfield of computational linguistics that is focused on translating text from one language to another. With the power of deep learning, Neural Machine Translation (NMT) has arisen as the most powerful algorithm to perform this task.
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- NMT Systems Understand Similarities Between Words
- NMT Systems Consider Entire Sentences
- NMT Systems Learn Complex Relationships Between Languages
- 3 Problems with NMT
Noting that his presentation will contain simplifications for a more general audience, Läubli laid out his first reason why NMT was a breakthrough: “NMT systems really have a notion of how similar words are.” He explained that both SMT and NMT, in the simplest sense, function using numerical substitution—i.e. they replace words with numbers, and th...
The next reason why NMT was a breakthrough, according to Läubli, was because of how NMT models assess the fluency of output. SMT systems would evaluate the fluency of a sentence in the target language a few words at a time using an N-gram language model, Läubli said. “If we have an N-gram model of order 3, when it generates a translation, it will a...
For Läubli’s final reason what NMT is a breakthrough, he compared how SMT and NMT systems are trained. He explained that SMT systems have three separate main components: 1. the translation model that calculates the proper translation for words between languages 2. the reordering model that reorders words in the output, and 3. the language model, wh...
Not wanting to mislead the audience into thinking NMT is a “solved problem,” Läubli said research into the technology is ongoing. He also highlighted three of its own problems: First, NMT can only translate on a sentence by sentence basis. “When we translate text what we do is cut the text into individual sentences and then all of these sentences a...
Jan 1, 2020 · Understanding how and why NMT produces its translation result is important to figure out the bottleneck and weakness of NMT models. Designing better architectures. Designing a new architecture that better than Transformer is beneficial for both NMT research and production.
- Zhixing Tan, Shuo Wang, Zonghan Yang, Gang Chen, Xuancheng Huang, Maosong Sun, Yang Liu
- 2020
Nov 24, 2023 · Neural machine translation engines are trained with huge amounts of bilingual data, and customized with a significant set of sentences previously translated by humans who provide the...
Feb 21, 2021 · State-of-the-art NMT (sequence-to-sequence actually) models, primarily RNNs with attention mechanism and the Transformer (with a focus on the Attention mechanism and the self-attention ). What is byte-pair encoding (BPE)? How does it help us with the translation task? How to evaluate NMT systems? Interpretability with the attention map.
Jun 16, 2020 · 16 Jun 2020 » nmt. Neural Machine Translation (NMT) in-domain models outperform generic models for the “domain” on which they are trained. In other words, in-domain models can observe terminology and generate translations that are more in line with a specialized context. You can download the NMT models below. Enjoy!
