The past few years have seen a significant uptick in the use of conversational platforms, chatbots and virtual assistants (VA). The use cases vary as per the industry and needs, but it's certain that chatbots and virtual assistants are now becoming an integral part of the digital strategy of enterprises.
Chatbots and VA’s have human-like conversational abilities and to generate better user experiences, organizations are working round the clock to make human-bot conversations more humane and believable. With this, chatbots also need to be able to hold the conversation without losing original context.
What makes the conversation humane and believable?
It’s something known as the Dialogue Management component. This crucial element enables chatbots to comprehend contextual communications. Each conversation, dialogue and intent is critical in strengthening the chatbot’s effectiveness; It facilitates a conversational experience. A user’s experience with a chatbot/VA depends on the efficiency of the Dialogue Management component.
Dialogue Management determines the actual context of the dialogue. For example, a user might place an order with a coffee outlet “Can I get a mocha?” and the bot will take the order asking for the size of the mocha - short/tall. Then the user might change their mind and ask for “Change to Blueberry muffin”.
Here the onus lies on the bot to correctly interpret the new order and confirm before proceeding ahead. The bot needs to "remember" the intent it detects in the initial stages of the conversation and then interpret related entities detected in subsequent queries as belonging to that intent.
According to Gartner, by 2022, 20% of large enterprises will have dedicated dialogue designers as part of their user experience teams, up from under 1% (2019).
There is an emphasis to invest resources on the four technology-agnostic work streams of:
- Identifying intents
- Training Intent recognition
- Dialogue designing
Gartner also recommends selecting solutions or capabilities that have a robust learning loop with dialogue management as part of the architecture, enabling continuous improvement.
In a crowded market of over 1,500 Conversational AI platform (CAIP) vendors, where does Yellow.ai’s dialogue management stand?
Yellow.ai, the world’s leading conversational AI platform, has a unique standpoint on dialogue design and management. Their conversational studio takes a hybrid approach to generate responses using NLG, which auto-generates replies and variants while building the flow. Instead of using word vectors to understand the query, the NLU engine performs intent matching using sentence representations, which provides accurate detection and classification of intents.
The platform also comes with many fallbacks, including a knowledge base and unsupervised learning models, so that the chatbot/virtual assistant always maintains a good dialogue and keeps the conversation going until the human handoff occurs.
Have a sneak peek into the unique dialogue management system designed by Yellow.ai
6 Reasons Why Yellow.ai’s Dialogue Management is Unrivaled
With Yellow.ai’s NLU based dialogue management, enterprises can:
- Use the low code/no-code platform to create simple & complex conversational journeys for the bots.
2. Use a variety of nodes like pre-built templatised nodes, prompt-based, message-based, action-based, logic-based nodes etc to enhance the quality of conversations.
3. Use intent/entity/sentiment-based logic, which is unique as it leads to sentiment-based actions at every step of the conversation.
4. Easily integrate with 3rd party applications to further enhance conversations.
5. Take advantage of suggestions/errors thrown to the bot builders to fix the conversation flow so as to take it to a logical end.
6. Work on unstructured data with the help of Yellow.ai’s Document Cognition Engine which can ingest unstructured data in the form of web URLs, PDFs, files etc to generate dialogues & Q&As. The Document Cognition Engine will parse through the data, index it and generate questions & corresponding responses.
Yellow.ai’s NLU based dialogue management makes it extremely easy to enhance intent recognition which forms the base for a smooth and fruitful conversation.
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With over 10+ years of experience in the Marketing & Sales domain, Nitish is a product marketer where he focuses on developing effective marketing strategies and plans to communicate the features and benefits of yellow.ai's product stack. In his free time, he can be found running/cycling at odd hours, reading books, sleeping like a log or rooting for Liverpool FC to win the PL again!