Automated Machine Learning for Custom Language Understanding

 

Conversational systems are rapidly becoming a key component of solutions such as virtual assistants, customer care, and the Internet of Things. When we talk about conversational systems, we refer to a computer’s ability to understand the human voice and take action based on understanding what the user meant. What’s more, these systems won’t be relying on voice and text alone. They’ll be using sight, sound, and feeling to process and understand these interactions, further blurring the lines between the digital sphere and the reality in which we are living. Chatbots are one common example of conversational systems.

 

Chatbots are a very trendy example of conversational systems that can maintain a conversation with a user in natural language, understand the user’s intent and send responses based on the organization’s business rules and data. These chatbots use Artificial Intelligence to process language, enabling them to understand human speech. They can decipher verbal or written questions and provide responses with appropriate information or direction. Many customers first experienced chatbots through dialogue boxes on company websites. Chatbots also interact verbally with consumers, such as Cortana, Siri and Amazon’s Alexa. Chatbots are now increasingly being used by businesses to augment their customer service.

 

Language understanding (LU) is a very centric component to enable conversational services such as bots, IoT experiences, analytics, and others. In a spoken dialog system, LU converts from the words in a sentence into a machine-readable meaning representation, typically indicating the intent of the sentence and any present entities. For example, consider a physical fitness domain, with a dialog system embedded in a wearable device like a watch. This dialog system could recognize intents like StartActivity and StopActivity, and could recognize entities like ActivityType. In the user input “begin a jog”, the goal of LU is to identify the intent as StartActivity, and identify the entity ActivityType= ’’jog’’.

 

Historically, there have been two options for implementing LU, machine learning (ML) models and handcrafted rules. Handcrafted rules are accessible for general software developers, but they are difficult to scale up, and do not benefit from data. ML-based models are trained on real usage data, generalize to new situations, and are superior in terms of robustness. However, they require rare and expensive expertise, access to large sets of data, and complex Machine Learning (ML) tools. ML-based models are therefore generally employed only by organizations with substantial resources.

 

In an effort to democratize LU, Microsoft’s Language Understanding Intelligent Service, LUIS shown in Figure 1 aims at enabling software developers to create cloud-based machine-learning LU models specific to their application domains, and without ML expertise. It is offered as part of the Microsoft Azure Cognitive Services Language offering. LUIS allows developers to build custom LU models iteratively, with the ability to improve models based on real traffic using advanced ML techniques.

 

We capitalize on the continuous innovation of Microsoft in Artificial Intelligence and its applications to natural language understanding with research, science, and engineering efforts dating back at least 20 years or more. In this blog, we dive deeper into the LUIS capabilities to enable intelligent conversational systems. We also highlight some of our customer stories that show how large enterprises use LUIS as an automated AI solution to build their LU models. This blog aligns with the December 2017 announcement of the general availability of our conversational AI and language understanding tools with customers such as Molson Coors, UPS, and Equadex.

 

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