Semantic search helps chatbots answer more questions

Recent advancements in natural language processing (NLP), particularly the introduction of transformers in 2017, have significantly improved the capabilities of chatbots and virtual assistants, making them more accessible and cost-effective for businesses without requiring large research and development budgets. Companies like Rasa have played a pivotal role by providing tools to build sophisticated chatbots efficiently, such as a customer assistant for Hotel Atlantis in Dubai, which can intelligently respond to inquiries by utilizing semantic search to parse customer reviews. Despite these advancements, challenges remain, such as ensuring the chatbot distinguishes between official information and anecdotal customer reviews, and maintaining accuracy in its responses. An evaluation comparing a basic chatbot to one enhanced with semantic search showed a marked improvement in the true positive rate and F1 score, highlighting the potential of integrating semantic search to improve chatbot performance. However, the precision of these chatbots remains within the margin of error, indicating a need for further data and analysis, and emphasizing the importance of confidence intervals in evaluating performance.