We present a grounded neural dialogue model that successfully collaborates
with people in a partially-observable reference game. We focus on a setting
where two agents each observe an overlapping part of a world context and need
to identify and agree on some object they share. Therefore, the agents should
pool their information and communicate pragmatically to solve the task. Our
dialogue agent accurately grounds referents from the partner's utterances using
a structured reference resolver, conditions on these referents using a
recurrent memory, and uses a pragmatic generation procedure to ensure the
partner can resolve the references the agent produces. We evaluate on the
OneCommon spatial grounding dialogue task (Udagawa and Aizawa 2019), involving
a number of dots arranged on a board with continuously varying positions,
sizes, and shades. Our agent substantially outperforms the previous state of
the art for the task, obtaining a 20% relative improvement in successful task
completion in self-play evaluations and a 50% relative improvement in success
in human evaluations.

本文提出一个基于神经网络的对话模型，用于协同解决局部可见参考游戏。该模型通过结构化的参考解析器精准地理解对话内容，利用递归记忆处理复杂任务并采用合理的生成策略，大幅提高了任务完成率。实验结果表明，该模型相对于同领域前沿技术取得了显著的性能提升。