Towards Reframing Codenames for Computational Modelling and Creativity Support using Associative Creativity Principles

Zunjani, Faheem Hassan; Olteteanu, Ana-Maria

doi:10.1145/3325480.3325510

Cited by 4 publications

(4 citation statements)

References 9 publications

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“…If comRAT-V could solve the visual RAT, it would be interesting from a computational perspective to obtain a secondary measurement for the associative process in CreaCogs, in a form of a different task. Our initial work in the direction of a second task is the Codenames board game (Zunjani and Olteteanu, 2019).…”

Section: Discussionmentioning

confidence: 99%

A Visual Remote Associates Test and Its Validation

Olteteanu

Zunjani

2020

Front. Psychol.

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Section: Discussionmentioning

confidence: 99%

A Visual Remote Associates Test and Its Validation

Olteteanu

Zunjani

2020

Front. Psychol.

Self Cite

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“…Another method (Zunjani & Olteteanu, 2019) proposes a formalization of the Codenames task using a knowledge graph, but does not provide an implementation of their proposed recursive traversal algorithm. We found that recursive traversal does not scale to the computation required to run repeated evaluations of Codenames -for each blue word b, we must find each associated word w in the knowledge base that has similarity s(w, b) greater than some threshold t, and repeat this process every trial.…”

Section: Related Workmentioning

confidence: 99%

Playing Codenames with Language Graphs and Word Embeddings

Koyyalagunta¹,

Sun²,

Draelos

et al. 2021

jair

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Although board games and video games have been studied for decades in artificial intelligence research, challenging word games remain relatively unexplored. Word games are not as constrained as games like chess or poker. Instead, word game strategy is defined by the players’ understanding of the way words relate to each other. The word game Codenames provides a unique opportunity to investigate common sense understanding of relationships between words, an important open challenge. We propose an algorithm that can generate Codenames clues from the language graph BabelNet or from any of several embedding methods – word2vec, GloVe, fastText or BERT. We introduce a new scoring function that measures the quality of clues, and we propose a weighting term called DETECT that incorporates dictionary-based word representations and document frequency to improve clue selection. We develop BabelNet-Word Selection Framework (BabelNetWSF) to improve BabelNet clue quality and overcome the computational barriers that previously prevented leveraging language graphs for Codenames. Extensive experiments with human evaluators demonstrate that our proposed innovations yield state-of-the-art performance, with up to 102.8% improvement in precision@2 in some cases. Overall, this work advances the formal study of word games and approaches for common sense language understanding.

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“…Again, since evaluations from bots may not represent human judgments, our human evaluation is more realistic and extensive, conducted through Amazon Mechanical Turk with 1,440 total samples. Another method (Zunjani & Olteteanu, 2019) proposes a formalization of the Codenames task using a knowledge graph, but does not provide an implementation of their proposed recursive traversal algorithm. We found that recursive traversal does not scale to the computation required to run repeated evaluations of Codenames -for each blue word, we must find each associated word w in the knowledge base that have Association w,b greater than some threshold t, and repeat this process every trial.…”

Section: Related Workmentioning

confidence: 99%

Playing Codenames with Language Graphs and Word Embeddings

Koyyalagunta¹,

Sun²,

Draelos³

et al. 2021

Preprint

View full text Add to dashboard Cite

Although board games and video games have been studied for decades in artificial intelligence research, challenging word games remain relatively unexplored. Word games are not as constrained as games like chess or poker. Instead, word game strategy is defined by the players' understanding of the way words relate to each other. The word game Codenames provides a unique opportunity to investigate common sense understanding of relationships between words, an important open challenge. We propose an algorithm that can generate Codenames clues from the language graph BabelNet or from any of several embedding methods -word2vec, GloVe, fastText or BERT. We introduce a new scoring function that measures the quality of clues, and we propose a weighting term called DETECT that incorporates dictionary-based word representations and document frequency to improve clue selection. We develop BabelNet-Word Selection Framework (BabelNet-WSF) to improve BabelNet clue quality and overcome the computational barriers that previously prevented leveraging language graphs for Codenames. Extensive experiments with human evaluators demonstrate that our proposed innovations yield state-of-the-art performance, with up to 102.8% improvement in precision@2 in some cases. Overall, this work advances the formal study of word games and approaches for common sense language understanding.

show abstract

Towards Reframing Codenames for Computational Modelling and Creativity Support using Associative Creativity Principles

Cited by 4 publications

References 9 publications

A Visual Remote Associates Test and Its Validation

A Visual Remote Associates Test and Its Validation

Playing Codenames with Language Graphs and Word Embeddings

Playing Codenames with Language Graphs and Word Embeddings

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