Playing Codenames with Language Graphs and Word Embeddings
Name
sminton,+12665-Article+Source-27329-1-11-20210616.pdf
Description
Published version
Size
2.32 MB
Format
Adobe PDF
Checksum (MD5)
c0eb04f4988984d269d0fb4e1c6a415c
Author(s)
Koyyalagunta, Divya
Sun, Anna
Draelos, Rachel Lea
Rudin, Cynthia
Date Issued
June 23, 2021
Journal
Journal of Artificial Intelligence Research
Publisher
AI Access Foundation
Version
Final published version
Abstract
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.
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1613/jair.1.12665
