研究合作游戏中玩家如何用语言解决复杂问题。
Characterizing Language Use in a Collaborative Situated Game
- 收集了11.5小时的多人协作游戏语音对话数据。
- 发现空间指代、澄清修复和临时约定等独特语言现象。
- 公开发布包含音视频与游戏状态的完整语料库。
合作类视频游戏要求多名参与者在复杂环境中通过沟通与推理协同工作，是语言数据的丰富来源。我们构建了Portal Dialogue Corpus：来自热门游戏《传送门2》合作模式的11.5小时真人语音对话，共包含24.5万条语句。分析显示，玩家语言中存在大量现有闲聊或任务导向对话语料库中罕见的现象，如复杂空间指代、澄清与修正机制，以及临时约定的形成。为支持未来对复杂情境下协作式问题求解中语言使用的分析，我们公开发布该语料库，包含玩家视频、音频、转录文本、游戏状态数据，以及人工与自动标注的语言数据。
原文摘要 · Abstract (English)
Cooperative video games, where multiple participants must coordinate by communicating and reasoning under uncertainty in complex environments, yield a rich source of language data. We collect the Portal Dialogue Corpus: a corpus of 11.5 hours of spoken human dialogue in the co-op mode of the popular Portal 2 virtual puzzle game, comprising 24.5K total utterances. We analyze player language and behavior, identifying a number of linguistic phenomena that rarely appear in most existing chitchat or task-oriented dialogue corpora, including complex spatial reference, clarification and repair, and ad-hoc convention formation. To support future analyses of language use in complex, situated, collaborative problem-solving scenarios, we publicly release the corpus, which comprises player videos, audio, transcripts, game state data, and both manual and automatic annotations of language data.
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