Communicate to Play: Pragmatic Reasoning for Efficient Cross-Cultural Communication (2024)

Abstract: In this paper, we study how culture leads to differences in common ground and how this influences communication. During communication, cultural differences in common ground during communication may result in pragmatic failure and misunderstandings. We develop our method Rational Speech Acts for Cross-Cultural Communication (RSA+C3) to resolve cross-cultural differences in common ground. To measure the success of our method, we study RSA+C3 in the collaborative referential game of Codenames Duet and show that our method successfully improves collaboration between simulated players of different cultures. Our contributions are threefold: (1) creating Codenames players using contrastive learning of an embedding space and LLM prompting that are aligned with human patterns of play, (2) studying culturally induced differences in common ground reflected in our trained models, and (3) demonstrating that our method RSA+C3 can ease cross-cultural communication in gameplay by inferring sociocultural context from interaction.

## introduction an english speaker from the u.k. might refer to the storage space at the back of a car as the "boot", but an english speaker from the u.s. will likely take "boot" to mean a type of shoe. the confusion that would arise in communication between these speakers is an instance of pragmatic failure @xcite . when humans communicate, however, they can often resolve such confusion by reasoning about the cultural background of their conversation partner, and correctly interpreting "boot" to refer to the appropriate concept. our goal is to develop an ai system capable of pragmatic reasoning and able to adapt to new players during live interaction. existing research in cross-cultural communication focuses on single-turn interactions @xcite @xcite or centers primarily on knowledge of cultural values or norms @xcite . however, these works miss the central aspect of inferring and adapting to sociocultural context through interaction (e.g. an american might infer that their conversation partner is british and use this to understand what the british person means when they say "boot"). to fill this gap, we introduce our method rational speech acts for cross-cultural communication (rsa+c3) as illustrated in figure 1 . we study the effectiveness of our method by creating a test bed for culturally induced differences in common ground using the collaborative reference game codenames duet as described in section 4.1. first, we simulate players of codenames duet, using the dataset presented by @xcite as training data for different cultures in section 5. then, we show that these simulated players can reflect the cultural differences present in the dataset in section 6. finally, we test how well our simulated players of different cultures can play codenames with each other section 7. through these interaction experiments, we show that our method rsa+c3 can significantly improve the win rates of games of codenames duet over our baseline, showing that it is inferring socio-cultural context from the interaction. code for our experiments and to replicate our findings can be found at github.com/icwhite/codenames. ## related work we first discuss previous work that has expanded on the rational speech acts framework @xcite and language games as a method of analyzing human dialogues, specifically in the context of conveying information concisely based on shared context. ## pragmatic reasoning with the rsa framework and rsa+c3 we formalize and describe the rsa framework as articulated in @xcite and introduce our method rsa+c3. rsa formulates communication as a conversation between a listener and a speaker. for codenames duet, we treat the literal listener as the guesser and the pragmatic giver as the clue giver. ## task data and metrics we introduce the dataset, game, and metrics we utilize in this paper to model cross-cultural communication. ## modeling codenames players with word embeddings and llms we explore two approaches to modeling our giver and guesser; trained word embeddings and prompting llms. we find that our giver and guesser based on word embeddings consistently outperform the few-shot prompted llms in accuracy on the human-selected guesses and targets, as illustrated in figure 2 . ## incorporating cultural context into player models to model cross-cultural communication in codenames duet, we must first train models to reflect the cultural background of human players. in section 6.1, we do this by training word embeddings using the technique described in section 5.1 on data representing a specific demographic attribute (e.g. education). in addition, we demonstrate how few-shot prompting with cultural context can lead to higher performance, highlighting the influence of cultural priors on codenames gameplay. ## cross-cultural pragmatic reasoning in interaction in section 5 we implemented literal listeners, and then trained literal listeners to reflect specific cultural patterns in section 6. now, we perform pragmatic reasoning with a speaker who has a different cultural background. ## discussion using codenames duet as a testbed for studying cross-cultural communication, we demonstrated that our simulated players are capable of reflecting human gameplay and their sociocultural patterns. we utilize our player models reflecting different sociocultural backgrounds to emulate pragmatic failure in live gameplay. this enables us and future researchers to measure the collaborative ability between agents of different backgrounds -if the win rate of codenames duet is higher, then the difference in common ground is more easily overcome. as the full complexity of cross-cultural communication cannot only be captured through codenames duet, directions for future work include applying these techniques to more complex utterances with more nuanced cultural differences and studying the resulting interactive gameplay. overall, we find that introducing cultural context as a way for givers and guessers to communicate in codenames duet gameplay increases alignment with human data based on the subset of culture involved. our results across various methods of simulating players and different cross-sections of demographics demonstrate the significance of continuing to study the impact of cultural context in speaker and listener communication. ## limitations in our paper, we train models to reflect various cultural attributes as shown in figure 3 and evaluate our method rsa+c3 to resolve pragmatic failure due to cultural differences such as education level in figure 6 . however, the cultures are not equally represented in the cross-cultural codes dataset @xcite we used with the participants being majority white (78%) and liberal (58%). therefore some cultural differences are not as pronounced as they would be in a more balanced dataset. we encourage future work to study gameplay on diverse data and explore communication in gameplay on a broader range of cultural subsets. ## broader impacts statement while cultural context can be a useful tool in informing clue generation and target selection in games like codenames, we caution against leaning heavily on these demographics due to the potential for stereotype-based associations. previous work has demonstrated the propensity for language models to incorporate biases into generations @xcite . although we are interested in seeing future work explore how culture can inform communication, allowing for both speakers and listeners to update their mental models of the other conversational participant, we acknowledge that leaning too heavily on these demographics can lead to potentially harmful assumptions.