We introduce a new language learning setting relevant to building adaptive
natural language interfaces. It is inspired by Wittgenstein's language games: a
human wishes to accomplish some task (e.g., achieving a certain configuration
of blocks), but can only communicate with a computer, who performs the actual
actions (e.g., removing all red blocks). The computer initially knows nothing
about language and therefore must learn it from scratch through interaction,
while the human adapts to the computer's capabilities. We created a game in a
blocks world and collected interactions from 100 people playing it. First, we
analyze the humans' strategies, showing that using compositionality and
avoiding synonyms correlates positively with task performance. Second, we
compare computer strategies, showing how to quickly learn a semantic parsing
model from scratch, and that modeling pragmatics further accelerates learning
for successful players.

通过和计算机交互完成任务，以语言游戏的方式，人类可以在不知道什么是语言的情况下学习语言，为建立自适应的自然语言接口打下基础，使用组合性而避免使用同义词有助于任务表现，从零开始快速学习语义解析模型，并进一步加速的学习计算机策略，以适应成功的玩家。