Building intelligent conversational agents with transfer learning and cognitive automation

Description

Current conversational agents and chatbots are limited in their functionality, as they have to be explicitly trained via sample user sentences (utterances) that are mapped to customer defined actions (intents). Given the variations on how humans might ask a given question, it’s not feasible to provide all possible utterances, limiting the conversational agent’s ability to understand and interpret user inputs. For example, if I ask Alexa, “Is it cold tomorrow?” it correctly understands the utterance and maps it to the weather intent and tells me the weather; however if I ask, “Do I need to wear my sweater tomorrow?” it can’t correctly interpret the utterance. Similarly, robotic process agents (RPA) are great for predefined rule-based tasks, actual interactions with a user don’t always follow a script.

Recent advances in deep learning frameworks for NLP, including ULMFiT, ELMo, and Open AI transformer, allow models to go beyond the shallow representations provided by word vectors to provide deep hierarchical representations of language, allowing the capture of high-level semantic concepts. Just like the ImageNet challenge allowed the emergence of generalizable model features for image classification across different categories, these frameworks enable the creation of models that learn the higher-level nuances of language, vastly improving natural language understanding (NLU). This advance in NLU allows a conversational agent to leverage pretrained models to understand that “wearing a sweater” is associated with cold weather and ask a clarifying question, “Do you want to check the weather for tomorrow?"

Sumeet Vij and Matt Speck showcase an innovative application of deep learning to power cognitive conversational agents. You’ll learn how chatbots can overcome the limitations of limited training datasets by leveraging transfer learning and deep pretrained models for NLP and how machine learning can advance robotic process automation (RPA) from “robotic” to “cognitive” automation.