Comparing Models of Associative Meaning: An Empirical Investigation of Reference in Simple Language Games
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Abstract (arXiv)
Simple reference games are of central theoretical and empirical importance in the study of situated language use. Although language provides rich, compositional truth-conditional semantics to facilitate reference, speakers and listeners may sometimes lack the overall lexical and cognitive resources to guarantee successful reference through these means alone. However, language also has rich associational structures that can serve as a further resource for achieving successful reference. Here we investigate this use of associational information in a setting where only associational information is available: a simplified version of the popular game Codenames. Using optimal experiment design techniques, we compare a range of models varying in the type of associative information deployed and in level of pragmatic sophistication against human behavior. In this setting, we find that listeners' behavior reflects direct bigram collocational associations more strongly than word-embedding or semantic knowledge graph-based associations and that there is little evidence for pragmatically sophisticated behavior by either speakers or listeners of the type that might be predicted by recursive-reasoning models such as the Rational Speech Acts theory. These results shed light on the nature of the lexical resources that speakers and listeners can bring to bear in achieving reference through associative meaning alone.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Oct 2018.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 0; 2024: 2.
5 papers.
- 2022WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Modelscited by 20
- 2021Playing Codenames with Language Graphs and Word Embeddingscited by 6
- 2024Using Game Play to Investigate Multimodal and Conversational Grounding in Large Multimodal Modelscited by 3
- 2022Towards automating Codenames spymasters with deep reinforcement learningcited by 1
- 2024Adapting to Teammates in a Cooperative Language Gamecited by 1
- 2024Using Game Play to Investigate Multimodal and Conversational Grounding in Large Multimodal Models20 Jun 2024
- 2024Adapting to Teammates in a Cooperative Language Game26 Feb 2024
- 2022Towards automating Codenames spymasters with deep reinforcement learning28 Dec 2022
- 2022WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Models25 Jul 2022
- 2021Playing Codenames with Language Graphs and Word Embeddings12 May 2021