We then use this score to decide whether or not to respond. Once this model classifies a message as a question, we send the message to our Q&A models, which output a probability score indicating the likelihood of belonging to a certain intent in the bot’s topic scope. Our binary convolutional neural network ( CNN) text classifier can successfully filter out “statements” - replies, announcements, etc. If the scope changes frequently and you lack the resources to constantly update this model’s training data to cover new topics and intents, you can create a more general question vs. Develop a binary classifier: If the topic scope stays constant, a simple solution is to build a model to distinguish between two classes of messages, relevant (within topic scope) and irrelevant (outside topic scope).To prevent unwanted responses, it’s important for the bot to respond only when a posted message is relevant to the training data. The training data determine the topics a bot is able to cover, and anything outside of that scope is irrelevant to the bot. Below are a few suggestions for how to determine relevance.įor chatbots, training data refers to example user queries (known as utterances) that a bot receives as well as the associated responses to each query given the user’s goal (known as intent). But in a community setting, most users intend to interact with other users, asking and answering questions while the bot listens in. In a one-on-one setting, anything the user says is directed at the chatbot and therefore must be responded to. Integrating a chatbot into a community forum In this article, we discuss the strategies we’ve found for implementing bots in community settings where the number of topics and the size of individual topics are constantly expanding. This gives those asking questions immediate answers - and lets the community members who are most likely to respond with the ability to focus on other aspects of their jobs. Today, our bot responds to FAQs in over 150 internal Capital One Slack channels. Within these channels, many of the same questions get asked repeatedly, and responding to them takes valuable time. Within each channel, users post about the channel’s designated topic. Capital One’s internal Slack has hundreds of channels dedicated to topics ranging from deploying software to corporate travel. But to assist more users, we expanded the bot’s reach to our most-used support forum: - Slack. The bot originally existed solely as a direct message experience and covered two topics. The Capital One team built a chatbot to answer associates’ questions about internal tools and processes. This value can also be brought to community forums to address queries that span a wide array of constantly expanding topics. Single-topic direct-message chatbots bring incredible value through time saved for users and companies. For example, the COVID-19 bot helps identify COVID symptoms, and it does not know how to answer questions outside the topic of COVID-19 such as “where to buy a bicycle?” We typically interact with chatbots in a direct message setting where the bot helps us accomplish a limited set of highly specific tasks about one particular subject matter (or topic).
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