NEW YORK, NY / ACCESSWIRE / October 23, 2020 / Chatbots, computer programs that interact with users through natural language, have become extraordinarily popular due to technological advances. Among various types of chatbots, the need of conversational AI chatbots has become acute in order to facilitate human computer interactions through messaging applications, phones, websites, or mobile apps. In fact, a chatbot is just one of the typical examples of AI systems, many of which are powered by machine learning.
According to a survey by Usabilla in 2019, 54% of respondents said they would prefer a chatbot to a human customer support representative if it saves them 10 minutes. Moreover, 59% of consumers in a PWC survey mentioned they want to have more humanized experiences with chatbots. Although customers have positive feelings toward AI solutions for the efficiency, many not-so-intelligent chatbots which are not smart enough to engage in fundamental conversations are seen in several industries.
Most chatbots that we see today are based on machine learning. They incorporate the ability to understand human language and get themselves trained. With machine learning, the computer systems can learn by being exposed to many examples: the training dataset. The dataset can be thought of as a set of examples. The chatbot's algorithms extract and save patterns with each input of data. In this way, a chatbot uses training data to understand user behavior and presents the most applicable conversation for a personalized experience.
If not properly considered and developed, chatbots may contain massive potential failures. For example, when a customer starts a conversation with the word "howdy", if the chatbot only has greeting words "hello", "hi" programmed in the training dataset, unfortunately it doesn't have a clue how to respond.
The quality of training data is key
Ben Virdee-Chapman, head of product at Kairos.com, once said that "the quality of the trainig data that allows algoritms to learn is key." Preparing the training dataset for chatbots is not easy. For a customer service chatbot, a dataset that contains a massive amount of discussion text between customers and human-based customer support need to be collected, cleaned and labeled to make it understandable for NLP and develop the AI-enabled chatbot so as to communicate with people.
Conversational AI agents such as Alexa and Siri are built with manually annotated data. The accuracy of the ML models can be constantly improved by manually transcribed and annotated data. However, large-scale manual annotation is usually expensive and time-consuming. Thus, abundant and useful datasets are valuable assets for chatbot development.
Manual annotation gives the chatbot a competitive advantage and differentiates it from other competitors. AI and ML companies are seeking high quality datasets to train their algorithms. The choices among different labeling services can make an enormous impact on the quality of the training data, the amount of time and cost required.
Chatbots needs sufficient data to understand human intention. Traditional data providers collect text data or transcribe audio data offline from all available resources and upload the total data onto a certain software, which in turn takes unnecessary communication cost. On the other hand, data quality is often not guaranteed. Thus, obtaining task-oriented text datasets and getting them annotated in a massive amount remains a bottleneck for developers.
ByteBridge.io is one of the leading data service companies that aim to transform the data labeling industry. With a unique and user-friendly platform, Bytebridge.io enables users to complete data labeling tasks online conveniently. Moreover, the blockchain-driven data company substitutes the traditional model with an innovative and precise consensus mechanism, which dramatically improves working efficiency and accuracy.
Partnered with over 30 different language speaking communities across the globe, ByteBridge.io now provides data collection and annotation services covering languages such as English, Chinese, Spanish, Korean, Bengali, Vietnamese, Indonesian, Turkish, Arabic, Russian and more. With rich access to contractors worldwide, it ensures training data quality while expanding its service to a wider range of locations. ByteBridge's high-quality data collecting and labeling service has empowered various industries such as healthcare, retail, robotics and self-driving, making it possible to integrate AI into such fields.
Chatbots are evolving and becoming increasingly sophisticated in an endeavor to mimic how people talk. Good chatbot applications can not only enhance customer experience, but also improve operational efficiency by reducing cost. To be successful, obtaining crucial datasets is valuable in training and optimizing the chatbot system.
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SOURCE: TTC Foundation
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