Chatbots need to have contextual awareness if they have to sufficiently resolve a query. Conversational UX relies on effective contextual intelligence to handle millions of queries over periods to create more meaningful relationships with customers.
Designing a Contextual Chatbot
Embedding contextual analysis is an important first step from the beginning. Besides, designing a contextual chatbot requires a strategic plan about key characteristics and use-cases for the technology.
Planning can be optimized by analyzing existing features & area and mapping out future requirements for specific use cases. Through this process, various technology integrations can be put in place to ensure that there is congruence.
Added to that, a contextual chatbot requires an integrated approach, massive data lakes and the right analytics methodology to design and development.
Training With the Right Data Sets
To make chatbots more contextually intelligent, you need to provide the right data sets. It can source the true meaning behind critical keywords to strengthen its neural network. This makes the context richer, especially in the case of customer-oriented chatbots.
The probability model needs the right type of data to adequately perform significant functions. The chatbot 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.
Integrating the Right Technologies
Whether that’s open-source technologies or vendor-driven systems, you need to find the right integrated technologies. The contextual chatbot needs complex technology such as Tensorflow or Dialogflow to handle complex queries.
Chatbots must be able to store and retrieve important information related to the context being analyzed. Moreover, you can even timecode the interaction to extract information using for analysis. For example, you can understand the type of action that is being conveyed in a query or a sentence. Then it conveys the right response based on this action-oriented model.
Applying NLP and Deep Learning
Traditional chatbots rely on a retrieval-based model that works within specified parameters. But the contextual chatbots require a more powerful NLP network and greater access to deep learning resources so that the system can learn key phrases to implement to create new chains of responses.
When chatbots are having thousands of simultaneous conversations, they automatically iterate on the best responses by using NLP. NLP helps to understand new information as it enters the systems and you can test out various responses
Sophisticated data mining tools are also required to ensure that there is a deeper context for every dialogue had. Through greater resource allocation and complex analytical models, the incoming data can be parsed and categorized effectively into segments that are run independently through the network. After that, nodes can act as connectors to ensure that there is a contextual response given to each query.
Differentiating between Sessions Context vs User Context
The chatbot needs to be complex enough to capture bigdata about the conversations as well as the customer’s profile for session context, which helps it to be more contextually intelligent and relevant for customers.
When moving on from subject to subject, chatbots are contextually aware and create the most suitable responses on the context detected. As a result, this leads to the retention of the customer while engaging them in a more meaningful conversation.
In conclusion, to make chatbots become more intelligent, you need to analyze large data sets through a more complex model in real-time to understand the context. Chatbots also need constant training to enhance the ability to respond appropriately because each industry has unique requirements.
Source: DZone
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