How State Governments are Taking Advantage of Conversational AI

If you’ve ever tried contacting your local government representative, you probably noticed that it is difficult to get through to the person you’re trying to reach. One reason could be the sheer volume of calls offices of government officials are dealing with. After all, members of the U.S. House of Representatives each represent a portion of their state known as a Congressional District, which averages 700,000 people. Senators however, represent the entire state.

One local government has found an interesting solution to get constituents answers to their questions by using conversational AI. Let’s take a closer look at this technology and the data annotation that’s required to create it. 

Launching Conversational AI at City Halls 

A local government in Easton, PA is launching a new tool next week from a company called Citibot which uses an AI chat bot to help residents find answers or locate the department they need to report problems. When people access the department’s website and ask Citibot a question, it can hold a conversation with them and provide them with a lot of useful information. Even if somebody asked Citibot a question it could not answer, it would route the question to a human representative at city hall. 

What are the Components Necessary to Create Conversational AI?

Conversational AI combines natural language processing (NLP) with machine learning. These NLP processes flow into a constant feedback loop with machine learning processes to continuously improve the AI algorithms. Conversational AI has principal components that allow it to process, understand, and generate responses in a natural way. Natural language processing is the current method of analyzing language with the help of machine learning used in conversational AI. Before machine learning, the evolution of language processing methodologies went from linguistics to computational linguistics to statistical natural language processing. In the future, deep learning will advance the natural language processing capabilities of conversational AI even further.

NLP consists of four steps: 

• Input generation: Users provide input through a website or an app; the format of the input can either be voice or text.
• Input analysis: If the input is text-based, the conversational AI solution app will use natural language understanding (NLU) to decipher the meaning of the input and derive its intention. However, if the input is speech-based, it’ll leverage a combination of automatic speech recognition (ASR) and NLU to analyze the data.
• Dialogue management: During this stage, Natural Language Generation (NLG), a component of NLP, formulates a response
• Reinforcement learning: Finally, machine learning algorithms refine responses over time to ensure accuracy

What Types of Data Annotation is Required to Create Conversational AI? 

As mentioned earlier, conversational AI leverages NLP to understand human language. For example, this can be entity annotation which teaches NLP models how to identify parts of speech, named entities and keyphrases within a text. This requires human data annotators to carefully analyze the training dataset and  locate the target entities, highlight them on the annotation platform and choose from a predetermined list of labels. To help NLP models learn about named entities further, entity annotation is often paired with entity linking. 

For more advanced conversational AI products, sentiment annotation may be necessary which involves labeling emotions, opinions, or sentiments inherent within a body of text. Annotators are given texts to analyze and must choose which label best represents the emotion or opinion within the text. This can be something like customer reviews in which the annotators would read the reviews and label them as positive, neutral or negative.

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