We, therefore, recommend the bot-building methodology to include and adopt a horizontal approach. What happens if the user asks the chatbot questions outside the scope or coverage? This is not uncommon and could lead the chatbot to reply “ Sorry, I don’t understand” too frequently, thereby resulting in a poor user experience. What are the best practices to build a strong dataset?įor a chatbot to deliver a good conversational experience, we recommend that the chatbot automates at least 30-40% of users’ typical tasks. incremental changes that were made to the data set and how it led to the improved performance levels). If you have joint owners, both owners need to fully understand and know the data set – intents, expressions, labeling of entities (free or restricted), and the evolution (i.e. If multiple owners exist, it could get tricky and challenging. Secondly, we recommend a single owner for the data set, that is in charge of monitoring and enhancing the bot dataset. Understand his/her universe including all the challenges he/she faces, the ways the user would express himself/herself, and how the user would like a chatbot to help. It is pertinent to understand certain generally accepted principles underlying a good dataset.įirstly, always place yourself in the users’ shoes. What are the core principles to build a strong dataset? In this article, we will understand how, using the SAP Conversational AI platform, we can build a good base data (aka training data) to train the chatbot, make sense of the data by efficient labeling, and the broad methods to develop a well-performing data set. Often, it forms the IP of the team that is building the chatbot. Building a data set is complex, requires a lot of business knowledge, time, and effort. Knowing how to train and actual training isn’t something that happens overnight. A chatbot with little or no training is bound to deliver a poor conversational experience. You can’t just launch a chatbot with no data and expect customers to start using it. That’s how we build the training data.Ĭhatbots are only as good as the training data they are given. Where no training data exists, we use the crowdsourcing method and ask representative users to ask the bot questions they would like their bot to meaningfully respond. To work out those answers, it will use data from previous conversations, emails, telephone chat transcripts, and documents, etc. The two key bits of data that a chatbot needs to process are (i) what people are saying to it and (ii) what it needs to respond to.įor example, in the case of a simple customer service chatbot, the bot will need an idea of the type of questions people are likely to ask and the answers it should be responding with. Therefore, building a strong data set is extremely important for a good conversational experience.įundamentally, a chatbot turns raw data into a conversation. Data is key to a chatbot if you want it to be truly conversational.
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