A Zero-Shot Transformer Model For an Attribute-Guided Challenge

Nicos Isaak

2023

Abstract

The Taboo Challenge competition, a task based on the well-known Taboo game, has been proposed to stimulate research in the AI field. The challenge requires building systems able to comprehend the implied inferences between the exchanged messages of guesser and describer agents. A describer sends pre-determined hints to guessers indirectly describing cities, and guessers are required to return the matching cities implied by the hints. Climbing up the scoring ledger requires resolving the highest number of cities with the smallest number of hints in a specified time frame. Here, we present TabooLM, a language-model approach that tackles the challenge based on a zero-shot setting. We start by presenting and comparing the results of our approach with three studies from the literature. The results show that TabooLM achieves SOTA results on the Taboo challenge, suggesting that it can guess the implied cities faster and more accurately than existing approaches.

Paper Citation

in Harvard Style

Isaak N. (2023). A Zero-Shot Transformer Model For an Attribute-Guided Challenge. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-623-1, pages 821-828. DOI: 10.5220/0011782000003393

in Bibtex Style

@conference{icaart23,
author={Nicos Isaak},
title={A Zero-Shot Transformer Model For an Attribute-Guided Challenge},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2023},
pages={821-828},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011782000003393},
isbn={978-989-758-623-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - A Zero-Shot Transformer Model For an Attribute-Guided Challenge
SN - 978-989-758-623-1
AU - Isaak N.
PY - 2023
SP - 821
EP - 828
DO - 10.5220/0011782000003393