#neural machine translation

Joey NMT provides a streamlined and accessible platform for gaining insights into neural machine translation (NMT). The framework focuses on implementing RNN and Transformer architectures using PyTorch, which aids beginners in exploring the fundamentals of NMT and adapting model structures or training protocols. Emphasizing readability over rapid feature deployment, Joey NMT includes functionalities like recurrent and transformer encoders, various attention mechanisms, multilingual translation capabilities with language tags, and evaluation metrics such as BLEU. Compatible with Python 3.11, PyTorch 2.1.2, and CUDA 12.1, it is simple to install via pip or its GitHub repository.

Discover how Bergamot Translator leverages the Marian NMT framework to enhance client-side machine translation in browsers. Access comprehensive build guides for both native and WebAssembly environments, enabling seamless integration into web applications. The project focuses on improving translation efficiency and speed, particularly for Firefox Nightly. Utilize Emscripten for intuitive WASM development, facilitating robust translation features in browser-based projects.

This survey examines the issue of hallucination in large language models, focusing on how generated content can diverge from user inputs or factual information. It reviews evaluation methods, explanation frameworks, and mitigation strategies, addressing input-conflicting, context-conflicting, and fact-conflicting hallucinations. The article highlights studies and benchmarks across domains such as machine translation and summarization, providing insights into research that seeks to enhance the factual reliability of these models.