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Challenges of Building an Intelligent Chatbot

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Challenges of Building an Intelligent Chatbot

Some of the challenges of building an intelligent chatbot include context integration, coherent responses, model assessment, and reading intention.

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Building an intelligent chatbot is not devoid of challenges. From making the chatbot context-aware to building the personality of a chatbot, there are challenges involved in making the chatbot intelligent.

Chatbot Challenges

Let's talk about context integration, coherent responses, model assessment, and reading intention.

Context Integration

Sensible responses are the holy grail of chatbots. Integrating context into the chatbot is the first challenge to conquer. In integrating sensible responses, both the physical context and the linguistic context must be integrated. For incorporating linguistic context, conversations are embedded into a vector, which presents a challenge. Contextual data, location, time, date, details about users, and other such data must be integrated with the chatbot.

AI chatbot Mitsuku

Coherent Responses

Achieving coherence is another hurdle to cross. The chatbot must be powered to answer consistently to inputs that are semantically similar. For instance, an intelligent chatbot must provide the same answer to queries like Where are you from? and Where do you reside? Though it looks straightforward, incorporating coherence into the model is indeed a challenge. The secret is to train the chatbot to produce semantically consistent answers.

Model Assessment

How is the chatbot performing?

The answer to this query lies in measuring whether the chatbot performs the task that it has been built for. Measuring is a challenge because there is a reliance on human judgment. Because the chatbot is built on an open domain model, it becomes increasingly difficult to judge whether the chatbot is performing its task. There's no specific goal attached to the chatbot. Moreover, researchers have found that some of the metrics used in these cases cannot be compared to human judgment.

Reading Intention

In some cases, reading intention becomes a challenge. Take generative systems, for instance. They provide generic responses for several user inputs. The ability to produce relevant responses depends on how the chatbot is trained. Without being trained to meet specific intentions, generative systems fail to provide the diversity required to handle specific inputs.

Planning to Use NLP and Machine Learning

Another factor that deserves attention is the plan to leverage NLP or machine learning for building your intelligent chatbot. Natural language processing is about finding answers by parsing language into intents, entities, agents, actions, and contexts. With NLP as the driving force, platforms like WIT, API, and LUIS can be leveraged to build an intelligent chatbot.

While you plan to leverage machine learning to create your own NLP, you must decide upon the model prior to building the intelligent chatbot. It is important to weigh generative and retrieval-based models as well as open and closed domains to create the intelligent chatbot that you have in mind.

Is an Intelligent Platform an Alternative to an Intelligent Chatbot?

Building an intelligent chatbot is one school of thought. Building a chatbot on an intelligent platform is altogether a different one. Today, several of successful chatbots (including x.ai and Google Assistant) have been built on intelligent platforms. In this scenario, the platform becomes the intelligent agent and the chatbot becomes a sensor for this intelligent agent.

The intelligent platform works to find out the goal, collect user information, and process, store, and convert that information to realize the goal. At this point, the challenge is not about infusing intelligence into a chatbot but about creating an intelligent platform. The focus must fall on ways to define the goal and factor sense-think-act capabilities into the platform.

For now, the chatbot imperative is to meet user-centric tasks. For that to happen, the chatbot must be intelligent. It becomes even more challenging to build an intelligent chatbot when significant elements surrounding the building process arise. As we look into the future, intelligent chatbots will be built to rule the world of connections.

TrueSight is an AIOps platform, powered by machine learning and analytics, that elevates IT operations to address multi-cloud complexity and the speed of digital transformation.

Topics:
chatbot ,bot development ,ai ,machine learning ,nlp

Published at DZone with permission of Mitul Makadia. See the original article here.

Opinions expressed by DZone contributors are their own.

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