论文标题
通过接地上下文修饰符，务实的颜色能力产生颜色
Pragmatically Informative Color Generation by Grounding Contextual Modifiers
论文作者
论文摘要
上下文信息中的基础语言对于细粒度的自然语言理解至关重要。涉及接地上下文修饰符的一项重要任务是颜色产生。给定参考颜色“绿色”和修饰符“蓝色”，如何产生可以代表“蓝色绿色”的颜色？我们提出了一个计算实用主义模型，该模型将这项颜色生成任务制定为扬声器和听众之间的递归游戏。在我们的模型中，务实的扬声器原因是关于听众会做出的推论，从而产生一种修改后的颜色，该颜色在最大程度上提供了信息，以帮助听众恢复原始的指称。在本文中，我们表明，与其他最先进的深度学习模型相比，合并实用信息可提供显着改进的性能，这些模型在代表大型连续空间的颜色方面的务实推理和灵活性。对于在训练过程中参考颜色看不见的测试用例，对于参考颜色在训练期间看不见的测试案件的性能绝对增长了98％，在训练过程中的性能绝对增加了40％。
Grounding language in contextual information is crucial for fine-grained natural language understanding. One important task that involves grounding contextual modifiers is color generation. Given a reference color "green", and a modifier "bluey", how does one generate a color that could represent "bluey green"? We propose a computational pragmatics model that formulates this color generation task as a recursive game between speakers and listeners. In our model, a pragmatic speaker reasons about the inferences that a listener would make, and thus generates a modified color that is maximally informative to help the listener recover the original referents. In this paper, we show that incorporating pragmatic information provides significant improvements in performance compared with other state-of-the-art deep learning models where pragmatic inference and flexibility in representing colors from a large continuous space are lacking. Our model has an absolute 98% increase in performance for the test cases where the reference colors are unseen during training, and an absolute 40% increase in performance for the test cases where both the reference colors and the modifiers are unseen during training.
