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A Simple Language Model for Task-Oriented Dialogue
Ehsan Hosseini-Asl · Bryan McCann · Chien-Sheng Wu · Semih Yavuz · Richard Socher

Mon Dec 07 09:00 PM -- 11:00 PM (PST) @ Poster Session 0 #36

Task-oriented dialogue is often decomposed into three tasks: understanding user input, deciding actions, and generating a response. While such decomposition might suggest a dedicated model for each sub-task, we find a simple, unified approach leads to state-of-the-art performance on the MultiWOZ dataset. SimpleTOD is a simple approach to task-oriented dialogue that uses a single, causal language model trained on all sub-tasks recast as a single sequence prediction problem. This allows SimpleTOD to fully leverage transfer learning from pre-trained, open domain, causal language models such as GPT-2. SimpleTOD improves over the prior state-of-the-art in joint goal accuracy for dialogue state tracking, and our analysis reveals robustness to noisy annotations in this setting. SimpleTOD also improves the main metrics used to evaluate action decisions and response generation in an end-to-end setting: inform rate by 8.1 points, success rate by 9.7 points, and combined score by 7.2 points.

Author Information

Ehsan Hosseini-Asl (Salesforce Research)
Bryan McCann (Salesforce Research)
Chien-Sheng Wu (Salesforce Research)
Semih Yavuz (Salesforce)
Richard Socher (Salesforce)

Richard Socher is Chief Scientist at Salesforce. He leads the company’s research efforts and brings state of the art artificial intelligence solutions into the platform. Prior, Richard was an adjunct professor at the Stanford Computer Science Department and the CEO and founder of MetaMind, a startup acquired by Salesforce in April 2016. MetaMind’s deep learning AI platform analyzes, labels and makes predictions on image and text data so businesses can make smarter, faster and more accurate decisions.

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