Contextual Clarity: Generating Sentences with Transformer Models using Context-Reverso Data

To create a dataset for training the T5 model, we harness the power of that provides usage examples for words. We prepared a dataset in the form of (query word, context or example usage) by parsing Context-Reverso webpages based on a query word. Additionally, we trained t5-small, and t5-base models for generating context-sentences based on input words. This resource enables us to obtain diverse and contextually rich sentences that incorporate the target keywords. We have also developed an application for learning new English words with a generated context [Telegram bot]. Our method aims to address the challenges of generating extremely short contexts and mitigating ambiguity in sentence construction. Objective: To develop a model that can generate informative and contextually relevant sentence-contexts for a given set of keywords, benefiting natural language understanding and generation applications such as search engines, personal assistants, and content summarization.