Types of chatbots

Chatbots generally fall into a few broad categories, depending on what purpose they are designed to serve.

Response architecture models

First, chatbots can be categorized according to how they generate the response that gets returned to the user. The simplest approach is therule-basedmodel, where chatbot responses are entirely predefined and returned to the user according to a series of rules. This includes decision trees that have a clear set of possible outputs defined for each step in the dialog.

Next, there is theretrieval-basedmodel, where chatbot responses are pulled from an existing corpus of dialogs. Machine learning models, such as statistical NLP models and sometimes supervisedneural networks, are used to interpret the user input and determine the most fitting response to retrieve. Like rule-based models, retrieval-based models rely on predefined responses, but they have the additional ability to self-learn and improve their selection of response over time.

Finally,generativechatbots are capable of formulating their own original responses based on user input, rather than relying on existing text. This involves the use ofdeep learning, such as LSTM-based seq2seq models, to train the chatbots to be able to make decisions about what is an appropriate response to return.

While generative models are very flexible and powerful in that they are not confined to a predefined set of rules or responses, they are also significantly more challenging to implement. Training these chatbots require an abundance of data, and it is often unclear what gets used for their decision-making, making them more prone to grammatical errors and nonsensical replies. By contrast, retrieval-based models can guarantee the quality of the responses since they are predefined, but these chatbots are in turn restricted to language that exists within the training data.

Thus, chatbots often use a combination of the different models in order to produce optimal results. For example, a customer support chatbot may use generative models for creating open-ended small talk with the user, but then are able to retrieve professional, predefined responses for answering the user’s inquiries regarding the business or product.

Conversation domains

Chatbots can also be categorized based on the range of conversation topics they are able to cover.Closed domainchatbots, ordialog agents, are restricted to providing responses with a particular focus, such as booking a hotel room. Because they are designed with a specific goal in mind, these chatbots are often very efficient and have great success in accomplishing what they are intended to accomplish. The user-perceived quality is also high, because users don’t expect the chatbots to provide responses outside of the pre-established domain.

On the other hand,open domainchatbots, orconversational agents, are capable of exploring any range of conversation topics, much like how a human-to-human interaction would be. Many of these “companion bots” have filled the roles of a friend or therapist, allowing the user to connect with them on an emotional level. While they have great potential, open domain chatbots are challenging to implement and evaluate.

It is worthwhile to note that the term “chatbot” is sometimes reserved only for open domain conversational agents, but for the purpose of this course, we include closed domain dialog agents as well.

Initiatives

Another way chatbots can be categorized is by which side – the user or bot – is able to take initiative on the conversation. Looking back at our previous example on hotel search, notice how the user is free to provide their request in their own words, and the chatbot is able to identify and piece together the relevant keywords to answer the query. This is an example of amixed-initiativesystem, representative of a normal human-to-human conversation where all participants have the chance to take initiative.

By contrast, asystem-initiativesystem is one where the chatbot controls the conversation and explicitly asks for each piece of information, such as the date and location for the hotel booking. While this system is more straight-forward to implement because user response can be anticipated, it lacks the flexibility and naturalness that characterize a normal human dialog.

Into the future

Chatbots have come a long way. What started out as computers that attempted to mimic human conversation has grown into elaborate systems that are able to carry out a multitude of functions and goals. But this great power and potential doesn’t come without a price. As chatbots continue to evolve, there are also growing ethical concerns that arise. We will discuss this in more detail in the next article!

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