Abstract
We introduce a novel algorithm for generating
referring expressions, informed by human
and computer vision and designed to refer to
visible objects. Our method separates absolute
properties like color from relative properties
like size to stochastically generate a diverse
set of outputs. The algorithm mimics
the majority of human data in several visual
scenes, outperforming the well-known Incremental
Algorithm (Dale and Reiter, 1995) and
the Graph-Based Algorithm (Krahmer et al.,
2003; Viethen et al., 2008) across domains.
We additionally introduce a new evaluation
method that takes the proposed algorithm’s
non-determinism into account.
referring expressions, informed by human
and computer vision and designed to refer to
visible objects. Our method separates absolute
properties like color from relative properties
like size to stochastically generate a diverse
set of outputs. The algorithm mimics
the majority of human data in several visual
scenes, outperforming the well-known Incremental
Algorithm (Dale and Reiter, 1995) and
the Graph-Based Algorithm (Krahmer et al.,
2003; Viethen et al., 2008) across domains.
We additionally introduce a new evaluation
method that takes the proposed algorithm’s
non-determinism into account.
| Original language | English |
|---|---|
| Title of host publication | Proc of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies |
| Place of Publication | Atlanta, Georgia |
| Publisher | Association for Computational Linguistics |
| Publication status | Published - Jun 2013 |
Keywords
- generation of referring expressions
- stochastic generation