Browse all content tagged with Machine Translation
The BLEU score, or Bilingual Evaluation Understudy, is a critical metric in evaluating the quality of text produced by machine translation systems. Developed by IBM in 2001, it was a pioneering metric that showed a strong correlation with human assessments of translation quality. The BLEU score remains a cornerstone in the field of natural language processing (NLP) and is extensively used to assess machine translation systems.
Language detection in large language models (LLMs) is the process by which these models identify the language of input text, enabling accurate processing for multilingual applications like chatbots, translation, and content moderation.
Part-of-Speech Tagging (POS tagging) is a pivotal task in computational linguistics and natural language processing (NLP). It involves assigning each word in a text its corresponding part of speech, based on its definition and context within a sentence. The main objective is to categorize words into grammatical categories such as nouns, verbs, adjectives, adverbs, etc., enabling machines to process and understand human language more effectively.
The ROUGE score is a set of metrics used to evaluate the quality of machine-generated summaries and translations by comparing them to human references. Widely used in NLP, ROUGE measures content overlap and recall, helping assess summarization and translation systems.
Windowing in artificial intelligence refers to processing data in segments or “windows” to analyze sequential information efficiently. Essential in NLP and LLMs, windowing optimizes context handling, resource usage, and model performance for tasks like translation, chatbots, and time series analysis.
