2 research outputs found
Fuzzy Sets Across the Natural Language Generation Pipeline
We explore the implications of using fuzzy techniques (mainly those commonly
used in the linguistic description/summarization of data discipline) from a
natural language generation perspective. For this, we provide an extensive
discussion of some general convergence points and an exploration of the
relationship between the different tasks involved in the standard NLG system
pipeline architecture and the most common fuzzy approaches used in linguistic
summarization/description of data, such as fuzzy quantified statements,
evaluation criteria or aggregation operators. Each individual discussion is
illustrated with a related use case. Recent work made in the context of
cross-fertilization of both research fields is also referenced. This paper
encompasses general ideas that emerged as part of the PhD thesis "Application
of fuzzy sets in data-to-text systems". It does not present a specific
application or a formal approach, but rather discusses current high-level
issues and potential usages of fuzzy sets (focused on linguistic summarization
of data) in natural language generation.Comment: Paper features: 16 pages, 2 tables, 13 figure
Perspective-corrected Spatial Referring Expression Generation for Human-Robot Interaction
Intelligent robots designed to interact with humans in real scenarios need to
be able to refer to entities actively by natural language. In spatial referring
expression generation, the ambiguity is unavoidable due to the diversity of
reference frames, which will lead to an understanding gap between humans and
robots. To narrow this gap, in this paper, we propose a novel
perspective-corrected spatial referring expression generation (PcSREG) approach
for human-robot interaction by considering the selection of reference frames.
The task of referring expression generation is simplified into the process of
generating diverse spatial relation units. First, we pick out all landmarks in
these spatial relation units according to the entropy of preference and allow
its updating through a stack model. Then all possible referring expressions are
generated according to different reference frame strategies. Finally, we
evaluate every expression using a probabilistic referring expression resolution
model and find the best expression that satisfies both of the appropriateness
and effectiveness. We implement the proposed approach on a robot system and
empirical experiments show that our approach can generate more effective
spatial referring expressions for practical applications.Comment: Under review, 20 pages, 12 figure