Chatbots need to have contextual awareness if they have to adequately resolve a query. This contextual awareness leads to intelligence over time, by handling millions of queries over significant periods. Conversational UX relies on effective contextual intelligence to create more meaningful relationships with customers. From banking to health services, each industry has unique requirements from contextual chatbots that work with large data sets.
Designing a Contextual Chatbot
Designing a contextual chatbot requires strategically planning out key characteristics and use-cases for the technology. This includes any critical data points that it needs to analyze first, as well as any customer-based interactions it can start having early on. When designing the right chatbot, embedding contextual analysis is important from the get-go.
Planning is a critical component that needs to be fully optimized if a company is to leverage contextual intelligence. This is done by analyzing existing features & scope and mapping out future requirements. Through this process, various technology integrations can be put in place to ensure that there is congruence.
Additionally, the right resources must be put in place if you want to design a contextual chatbot. From the right teams to the right talent, a contextual chatbot requires an integrated approach when it comes to design and development. The chatbot will also require massive data lakes to scan through, which makes it even more critical to use the right analytics methodology.
Training With the Right Data Sets
When making chatbots more contextually intelligent, the key area to focus on is training. You need to provide the right data sets to the chatbot so that it can become more contextually aware. It can source the true meaning behind critical keywords to strengthen its neural network. This ultimately makes the context richer, especially in the case of customer-facing chatbots.
The probability model needs the right type of data to adequately perform critical functions. It needs to understand real conversations to be able to derive real responses to future queries. A high-quality intent classification model needs to be designed keeping focused raw data at the center of the process. For every x input, there can be responses a, b, c, etc. which means that the right response can only be drawn out by increasing the volume of conversational data.
Integrating the Right Technologies
Whether that’s open-source technologies or vendor-driven systems, finding the right technology is critical. Since the contextual chatbot needs to be sophisticated enough to handle complex queries, technologies such as Tensorflow and Dialogflow can play an important role. They help design complex chatbots that are contextually aware.
TensorFlow comes with the seq2seq module that can aid in the process of defining the right neural network. The resulting network can be stress-tested against complex queries to review the appropriate response by the machine.
Chatbots must also be able to store and retrieve critical information related to the context being analyzed. E.g., A JSON object can be used as a part of a larger string to store critical information about a user or a session. You can even timecode the interaction to then retrieve that information later on for analysis.
Intel AI Lab’s NLP Architect uses intent extraction to understand the type of action that is being conveyed in a query or a sentence. It focuses on the assigner and the assignee to drive meaningful insights. It then conveys the right response based on this action-oriented model. This is helping companies drive retention by integrating meaningful context to every query. It’s an open-source library that’s aiding in dependency parsing, slot tagging, memory network development, and word chunking.
NLP and Deep Learning to Power Contextual Intelligence
Chatbots require a more powerful NLP network and greater access to deep learning resources. Through this, the system can learn key phrases to implement to elicit an engaging response. Traditional chatbots rely on a retrieval-based model that works within certain parameters. Deep learning is especially powerful in the case of generative chatbots that don’t rely on pre-coded responses. Since the AI needs to create a novel response, it needs NLP and deep learning to create new chains of responses.
NLP is also required to understand new data as it enters the systems. When chatbots are having thousands of simultaneous conversations, they automatically iterate on the best responses. That’s why NLP is needed at scale to give chatbots the right infrastructure through which they can test out various responses.
Sophisticated data mining tools are also required to ensure that there is a deeper context to every conversation had. This is done through greater resource allocation and creating complex analytical models through which incoming data can be parsed. Inputs can then be categorized effectively into segments that are run independently through the network. Nodes can then act as connectors to ensure that there is a contextual response given to each query placed.
Sessions Context vs User Context
Sessions context is required to be analyzed just as much as the user information context. This means that the chatbot should be complex enough to capture metadata about the conversations they’re having as well as the user’s profile in general. Through the effective capturing of both user and session data, the chatbot is able to become more contextually intelligent.
This also makes the conversation more relevant for the customer as well. As they move on from subject to subject, they may have a pattern emerging in their conversation. Chatbots that are contextually aware, will read into these patterns and create the best responses on the spot. They can also share value-add services based on the context detected. This can have a direct impact on the retention of the customer while engaging them in a more meaningful dialogue.
In conclusion, for chatbots to become more intelligent they need to analyze greater data sets through a more sophisticated model. Additionally, chatbots also need to understand the context in real-time while analyzing insights from user-based information. Chatbots also need to iterate constantly to enhance the responses that they generate over time.