Abstract
We present a model for pragmatically describing scenes, in which contrastive behavior results from a combination of inference-driven pragmatics and learned semantics. Like previous learned approaches to language generation, our model uses a simple feature-driven architecture (here a pair of neural “listener” and “speaker” models) to ground language in the world. Like inference-driven approaches to pragmatics, our model actively reasons about listener behavior when selecting utterances. For training, our approach requires only ordinary captions, annotated without demonstration of the pragmatic behavior the model ultimately exhibits. In human evaluations on a referring expression game, our approach succeeds 81% of the time, compared to a 69% success rate using existing techniques.
Abstract (translated by Google)
我们提出了一个实用描述场景的模型，其中对比行为是由推理驱动的语用学和学习语义学的结合而产生的。像以前学习语言生成的方法一样，我们的模型使用简单的功能驱动架构（这里有一对神经“听者”和“扬声器”模型）来描述世界的语言。像推理驱动的语用学方法一样，我们的模型在选择话语时积极地聆听者的行为。对于培训，我们的方法只需要普通的标题，注释了模型最终展示的实用行为。在参考表达式游戏的人类评估中，我们的方法成功率为81％，而使用现有技术的成功率为69％。
URL
https://arxiv.org/abs/1604.00562