Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning to Generate Unambiguous Spatial Referring Expressions for
  Real-World Environments   Referring to objects in a natural and unambiguous manner is crucial for
effective human-robot interaction. Previous research on learning-based
referring expressions has focused primarily on comprehension tasks, while
generating referring expressions is still mostly limited to rule-based methods.
In this work, we propose a two-stage approach that relies on deep learning for
estimating spatial relations to describe an object naturally and unambiguously
with a referring expression. We compare our method to the state of the art
algorithm in ambiguous environments (e.g., environments that include very
similar objects with similar relationships). We show that our method generates
referring expressions that people find to be more accurate ($\sim$30% better)
and would prefer to use ($\sim$32% more often).

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The categories of this paper are: dg-ga
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The categories of this paper are: chao-dyn
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The categories of this paper are: econ
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The categories of this paper are: acc-phys
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The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: funct-an
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The categories of this paper are: q-alg
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The categories of this paper are: math-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: mtrl-th
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The categories of this paper are: bayes-an
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The categories of this paper are: stat
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The categories of this paper are: hep-lat
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The categories of this paper are: q-bio