使用 Codenames Duet 建模跨文化语用推理
Modeling Cross-Cultural Pragmatic Inference with Codenames Duet
AI总结：
本文构建基于 Codenames Duet 的 Cultural Codes 数据集，并联合建模玩家社会文化先验与游戏情境，证明背景信息能显著提升给线索和猜词行为预测。
摘要：
语用指称使高效的人际沟通成为可能。以往工作使用简单的指称博弈来测试语用推理模型，且通常涉及身份不明的说话者和倾听者。然而在实践中，说话者的社会文化背景会塑造其语用假设。例如，本文读者会认为 NLP 指的是“Natural Language Processing（自然语言处理）”，而非“Neuro-linguistic Programming（神经语言程序学）”。本工作引入 Cultural Codes 数据集，该数据集在一个简单的词语指称博弈中将社会文化语用推理操作化。Cultural Codes 基于多回合双人协作游戏 Codenames Duet。我们的数据集包含 794 局游戏、7,703 个回合，分布在 153 名独特玩家中。除游戏过程外，我们还收集了玩家的人格、价值观和人口统计信息。利用沟通与语用学理论，我们通过联合建模玩家的社会文化先验和游戏情境来预测每名玩家的行动。实验表明，考虑背景特征会显著提升模型在给线索和猜词两类相关任务上的表现，表明社会文化先验在游戏决策中发挥着至关重要的作用。
英文摘要：
Pragmatic reference enables efficient interpersonal communication. Prior work uses simple reference games to test models of pragmatic reasoning, often with unidentified speakers and listeners. In practice, however, speakers' sociocultural background shapes their pragmatic assumptions. For example, readers of this paper assume NLP refers to "Natural Language Processing," and not "Neuro-linguistic Programming." This work introduces the Cultural Codes dataset, which operationalizes sociocultural pragmatic inference in a simple word reference game. Cultural Codes is based on the multi-turn collaborative two-player game, Codenames Duet. Our dataset consists of 794 games with 7,703 turns, distributed across 153 unique players. Alongside gameplay, we collect information about players' personalities, values, and demographics. Utilizing theories of communication and pragmatics, we predict each player's actions via joint modeling of their sociocultural priors and the game context. Our experiments show that accounting for background characteristics significantly improves model performance for tasks related to both clue giving and guessing, indicating that sociocultural priors play a vital role in gameplay decisions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
