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How to Approach Data Collection for Conversational AI

Designing Dialogues For Conversational AIThe goal of AI has predominantly been replicating human behavior through gestures, actions, and responses. The conscious human mind has the innate ability to understand context, intent, tone, emotions, and other factors and respond accordingly. But how can machines differentiate these aspects? Designing dialogues for conversational AI is very complex and more importantly, quite impossible to roll out a universal model. Each individual has a different way of thinking, talking, and responding. Even in responses, we all articulate our thoughts uniquely. So, machines have to listen and respond accordingly. However, this is not smooth as well. When humans talk, factors like accents, pronunciation, ethnicity, language, and more come in and it is not easy for machines to misunderstand and misinterpret words and respond back. A particular word can be understood by machines in a myriad of ways when dictated by an Indian, a British, an American, and a Mexican. There are tons of language barriers that come into play and the most practical way to come up with a response system is through visual programming that is flowchart-based. Through dedicated blocks for gestures, responses, and triggers, authors and experts can help machines develop a character. This is more like an algorithm machine can use to come up with the right responses. When an input is fed, the information flows through corresponding factors, leading to the right response for machines to deliver. Dial D For DiversityLike we mentioned, human interactions are very unique. People around the world come from different walks of life, backgrounds, nationalities, demographics, ethnicities, accents, diction, pronunciation, and more. For a conversational bot or a system to be universally operable, it has to be trained with as diverse training data as possible. If, for instance, a model has been trained only with the speech data of one particular language or ethnicity, a new accent would confuse the system and compel it to deliver wrong results. This is not just embarrassing for business owners but insulting for users as well. That’s why the development phase should involve AI training data from a rich pool of diverse datasets composed of people from all possible backgrounds. The more accents and ethnicities your system understands, the more universal it would be. Besides, what would annoy users more is not incorrect retrieval of information but failure to understand their inputs in the first place. Eliminating bias should be a key priority and one way companies could do this is by opting for crowdsourced data. When you crowdsource your speech data or text data, you allow people from around the world to contribute to your requirements, making your data pool only wholesome (Read our blog to understand the benefits and the pitfalls of outsourcing data to crowdsource workers). Now, your model will understand different accents and pronunciations and respond accordingly. The Way ForwardDeveloping conversational AI is as difficult as raising an infant. The only difference is that the infant would eventually grow to understand things and get better at communicating autonomously. It’s the machines that need to be consistently pushed. There are several challenges in this space currently and we should acknowledge the fact that we have some of the most revolutionary conversational AI systems stemming out despite these challenges. Let’s wait and see what the future holds for our friendly neighborhood chatbots and virtual assistants. Meanwhile, if you intend to get conversational AI like Google Home developed for your business, reach out to us for your AI training data and annotation needs.

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