Self-consistent voting for low-resource language semantic parsing in MTOP

Abstract

Abstract: In recent years, fostered by deep learning technologies and by the high demand for conversational AI, various approaches have been proposed that address the capacity to elicit and understand user's needs in task-oriented dialogue systems. We focus on two core tasks, slot filling (SF) and intent classification (IC), and survey how neural based models have rapidly evolved to address natural language understanding in dialogue systems. We introduce three neural architectures: independent models, which model SF and IC separately, joint models, which exploit the mutual benefit of the two tasks simul

Research Question

Does self-consistent voting improve low-resource language performance in task-oriented semantic parsing on MTOP without additional fine-tuning?

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