150 Western Avenue, Allston, MA 02134

Machine learning systems have seen advancements due to large models pre-trained on vast amounts of data. These pre-trained models have led to progress on various downstream tasks when fine-tuned. However, for machine learning systems to function in real-world environments, they must overcome certain challenges that are not influenced by model or dataset sizes. One potential solution is to fine-tune machine learning models based on online interactions.

In this talk, I will present my research on developing natural language processing systems that learn from interacting in an environment. I will begin by describing the issues that arise when systems are trained on offline data and then deployed in interactive environments. Additionally, I will present an algorithm that addresses these issues using only environmental interaction without additional supervision. Moreover, I will demonstrate how learning from interaction can improve natural language processing systems. Finally, I will present a set of new interactive learning algorithms explicitly designed for natural language processing systems.

 

  • Layla Jeanne Seaver
  • Li Yao
  • Mot Harris Dunlop Stothart

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