An RNN-based translation model, for example, might have trouble remembering the gender of the subject of a long paragraph. Worse, RNNs were hard to train.

Most studies find that neural machine-translation models can translate only about 30 percent of novel excerpts—usually simple passages—with acceptable quality, as determined by native speakers.

Parsing involves breaking down a sentence into constituent parts, such as subject, object, verb, etc. Parsing is a crucial step in many NLP tasks, such as machine translation or text-to-speech ...

When you have limited time or you lack the data to train an NLP model, an out-of-the-box solution offers a couple of major advantages. It’s effective for quick proofs of concept and delivers ...

Machine translation is the process of automatically translating text or speech from one language to another. It has been around for decades, but recent advances in AI have led to a dramatic ...

Facebook today open-sourced M2M-100, an algorithm it claims is the first capable of translating between any pair of 100 languages without relying on English data. The machine learning model, which ...

NLP, a branch of AI, is embedded in daily tech like smartphones, enhancing user interaction. Modern NLP tools like GPT-3 can write and code, mimicking human responses with high accuracy. Investors ...

The Masakhane project works with AI researchers and data scientists across Africa, and the organization aims to create neural machine translation that connects Africa’s many populations.

NLP began in the 1950s as machine translation (MT). These early MT efforts were intended to aid in code-breaking during World War II. Developers hoped MT would translate Russian into English, but ...