Explore the crucial role of language data in training and fine-tuning LLMs and GenAI, ensuring high-quality, context-aware translations, fostering the symbiosis of human and machine in the localization sector.
Domain Adaptation can be classified into three types - supervised, semi-supervised, and unsupervised - and three methods - model-centric, data-centric, or hybrid.
Text Summarization can be categorized under two types: Extraction and Abstraction. With the power of AI, summarization is becoming more popular and accessible.
Synthetic parallel data generation by back-translation as a solution for the problem of translating low-resource languages and texts from low-resource domains.
Natural Language Technologies are on the rise: making optimal use of NLT and its subcategories is crucial to remain up-to-date with the latest AI solutions
Which language data for AI trends you should expect to rise in 2022: expansion of multilingual AI data and models, more companies joining the data market, data diversity and lifelong learning machines.
Web scraping is a common way to generate parallel data, making use of the immense source of multilingual data offered on the web. Here is how to do web scraping.