How NLP Powers MADISON – Contextere's Insight Engine & Industrial Chatbot

Natural Language Processing (NLP) is a branch of artificial intelligence that enables computers to understand, interpret, and utilize human languages. In its early stages, NLP systems heavily relied on handcrafted rules and symbolic representations, which, in turn, limited their scalability and robustness. Since the 1980s, NLP has embraced machine learning techniques to learn from vast amounts of data and automatically extract linguistic patterns and features. In the 21st century, NLP has experienced rapid growth and improvement, owing to the availability of massive data on the web, advancements in computational power, and the emergence of deep learning algorithms. Neural networks, including recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformers, have consistently achieved state-of-the-art results in various NLP challenges, such as machine translation, speech recognition, information retrieval, text summarization, semantic analysis, question answering, and more. At contextere, we have harnessed NLP techniques to empower our customized virtual assistant, MADISON, which is tailored to customer-specific data.?

Conversational Agent?

Natural language generation (NLG), a subtask of NLP, involves generating natural language responses that are both appropriate and contextually relevant to a user's input during a conversation. Utilizing Large Language Models (LLMs), virtual assistants can now provide information, answer questions, and engage in meaningful dialogues with users, departing from the limitations of template-based or rule-based generation. Empowered by LLMs, MADISON can engage in natural and professional conversations with users.?

Semantic Search?

MADISON is not your typical entertainment chatbot. Its primary purpose is to deliver professional and precise recommendations for users within a working environment. By leveraging advanced NLP techniques to analyze user intentions and their correlation with the knowledge base, MADISON excels at comprehending user needs and offers more accurate responses compared to traditional search methods. LLM embeddings are employed to represent information within the knowledge base and assess the similarities between queries and documents or data sources, resulting in enhanced search and recommendation performance.?

Continuous Training and Fine-Tuning?

MADISON is designed to remain adaptable, continuously incorporating new knowledge from customers and feedback gathered through user interactions. The fine-tuning of the LLM enables the model to adjust to evolving data distributions, thereby acquiring relevant features and patterns for new knowledge or domains. By implementing reinforcement learning techniques and rewarding the model based on user feedback during training, MADISON is capable of learning from its own actions and outcomes. This process enables the model to explore different strategies and behaviors that align more closely with the needs of the user.?

NLP remains a dynamic and continually evolving field of research and development, replete with numerous open problems and exciting opportunities. We persist in integrating novel NLP technologies to enhance MADISON with additional features and elevate its performance. Among the ongoing projects at contextere are initiatives involving multimodal LLMs, which allow us to seamlessly handle inputs in various forms, including text, speech, images, and videos. Additionally, we are exploring the application of cross-lingual and low-resource NLP techniques to address languages with limited data and resources.

Written by: Lin Song, Machine Learning Specialist