GODEL: Combining goal-oriented dialog with real-world conversations

Article Source: Microsoft, Baolin Peng , Michel Galley , Pengcheng He , Chris Brockett , Lars Liden , Elnaz Nouri , Zhou Yu , Bill Dolan , Jianfeng Gao

They make restaurant recommendations, help us pay bills, and remind us of appointments. Many people have come to rely on virtual assistants and chatbots to perform a wide range of routine tasks. But what if a single dialog agent, the technology behind these language-based apps, could perform all these tasks and then take the conversation further? In addition to providing on-topic expertise, such as recommending a restaurant, it could engage in a conversation about the history of the neighborhood or a recent sports game, and then bring the conversation back on track. What if the agent’s responses continually reflect the latest world events? And what if it could do all of this without the need for any additional work by the designer?   

With GODEL, this may not be far off. GODEL stands for Grounded Open Dialogue Language Model, and it ushers in a new class of pretrained language models that enable both task-oriented and social conversation and are evaluated by the usefulness of their responses.  

Pretrained language models are among the engines that power conversational AI, the technology that underlies these dialog agents. They can either be task-oriented (“give me a job, and I’ll do it”) or engage in a conversation without a specified outcome, known as open-domain or chit-chat. GODEL combines both these capabilities, giving dialog agents the ability to generate responses based not just on the context of the conversation, but also on external information, content that was not part of the dataset when the model was trained. This includes both structured content, such as information stored in databases, and unstructured content, such as restaurant reviews, Wikipedia articles, and other publicly available material found on the web. This explains how a simple task-based query about restaurant recommendations can evolve into a dialog about ingredients, food, and even cooking techniques—the kind of winding path that real-world conversations take.  

In 2019, the Deep Learning and Natural Language Processing groups at Microsoft Research released DialoGPT, the first large-scale pretrained language model designed specifically for dialog. This helped make conversational AI more accessible and easier to work with, and it enabled the research community to make considerable progress in this area. With GODEL, our goal is to help further this progress by empowering researchers and developers to create dialog agents that are unrestricted in the types of queries they can respond to and the sources of information they can draw from. We also worked to ensure those responses are useful to the person making the query.    

In our paper, “GODEL: Large-Scale Pre-training for Goal-Directed Dialog,” we describe the technical details underlying GODEL, and we have made the code available on GitHub

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