What task framing did researchers like Perez and Liu adopt in the mid-to-late 2010s for Dialog State Tracking using deep learning models?
Answer
Machine Reading Comprehension (MRC) task.
With the substantial advancements in deep learning models, particularly those leveraging contextual embeddings like BERT, the approach to DST underwent another major transformation in the mid-to-late 2010s. Researchers began conceptualizing the DST problem as a Machine Reading Comprehension (MRC) task. In this formulation, the entire preceding conversation history is treated as the 'passage' of text. The system's goal, such as determining a slot value like price range, is modeled as answering a specific question against that passage. This framing allowed neural models, often using attention mechanisms, to perform complex extraction directly from the source text.
Related Questions
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