Abstract and Keywords
As robots become increasingly prevalent in our society, it becomes increasingly important to endow them with natural language capabilities, including the ability to both understand and generate so-called referring expressions. In recent work, we have sought to enable referring expression understanding capabilities by leveraging the Givenness Hierarchy (GH), which provides an elegant linguistic framework for reasoning about notions of reference in human discourse. This chapter first provides an overview of the GH and discusses previous GH-theoretic approaches to reference resolution. It then describes our own GH-theoretic approach, the GH-POWER algorithm, and suggests future refinements of our algorithm with respect to the theoretical commitments of the GH. Next, the chapter briefly surveys other prominent approaches to reference resolution in robotics, and discusses how these compare to our approach. Finally, it concludes with a discussion of possible directions for future work.
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