Learn Effective Dialog Representations by Self-Supervised Learning

Self-supervised model capable of learning various effective dialog representations

Artificial intelligence (AI), and machine-learning techniques, have proven to be highly promising in completing a variety of tasks. This includes those that require processing and generating languages. Language-related machine-learning models have allowed the creation of systems such as chatbots and smart speakers that can converse and interact with humans.

Language models must be able learn high-quality dialogs in order to tackle dialog-oriented problems. These are representations which summarize the ideas that are expressed by two people conversing on a specific topic and how they are structured.

Researchers from Northwestern University and AWS AI Labs developed a model for self-supervised learning that allows it to learn dialog representations for various dialog types. This model was introduced in a pre-published paper on arXiv and could be used to create more versatile dialog systems with a small amount of training data.


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