customer support chatbot training project
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Text SummarizationThe project involved annotating and labeling customer support chat conversations to support the training and improvement of a conversational AI chatbot. The scope included reviewing a large dataset of user–agent interactions, classifying user intents, tagging key phrases and entities, and evaluating chatbot responses for relevance and accuracy. Each conversation was carefully analyzed to ensure proper categorization across multiple support themes such as billing, technical issues, account management, and general inquiries. Labeling tasks performed included intent classification, text tagging, conversation categorization, and response quality assessment (relevant vs. irrelevant or good vs. poor responses). The project size involved thousands of chat records processed in batches to ensure consistency and efficiency. Quality measures adhered to strict annotation guidelines, including double-checking labeled outputs, maintaining consistency across similar cases, and performing periodic reviews to minimize errors and ensure high dataset accuracy for model training.