We present a model of pragmatic referring expression interpretation in a
grounded communication task (identifying colors from descriptions) that draws
upon predictions from two recurrent neural network classifiers, a speaker and a
listener, unified by a recursive pragmatic reasoning framework. Experiments
show that this combined pragmatic model interprets color descriptions more
accurately than the classifiers from which it is built, and that much of this
improvement results from combining the speaker and listener perspectives. We
observe that pragmatic reasoning helps primarily in the hardest cases: when the
model must distinguish very similar colors, or when few utterances adequately
express the target color. Our findings make use of a newly-collected corpus of
human utterances in color reference games, which exhibit a variety of pragmatic
behaviors. We also show that the embedded speaker model reproduces many of
these pragmatic behaviors.

该论文提出了一种基于递归语用推理框架的实用指称表达解释模型，利用两个循环神经网络分类器的预测来识别颜色，实验证明模型比分类器更准确地解释颜色描述，并且大部分改进来自于结合说话者和听者视角。