@inproceedings{shaikh-etal-2023-modeling,
    title = "Modeling Cross-Cultural Pragmatic Inference with Codenames Duet",
    author = "Shaikh, Omar  and
      Ziems, Caleb  and
      Held, William  and
      Pariani, Aryan  and
      Morstatter, Fred  and
      Yang, Diyi",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.410",
    doi = "10.18653/v1/2023.findings-acl.410",
    pages = "6550--6569",
    abstract = "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.",
}

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.
Shaikh, Omar Ziems, Caleb Held, William Pariani, Aryan Morstatter, Fred Yang, Diyi
Modeling Cross-Cultural Pragmatic Inference with Codenames Duet