@inproceedings{wang-etal-2024-understanding,
title = "Understanding Players as if They Are Talking to the Game in a Customized Language: A Pilot Study",
author = "Wang, Tianze and
Honarijahromi, Maryam and
Katsarou, Styliani and
Mikheeva, Olga and
Panagiotakopoulos, Theodoros and
Smirnov, Oleg and
Cao, Lele and
Asadi, Sahar",
editor = "Kumar, Sachin and
Balachandran, Vidhisha and
Park, Chan Young and
Shi, Weijia and
Hayati, Shirley Anugrah and
Tsvetkov, Yulia and
Smith, Noah and
Hajishirzi, Hannaneh and
Kang, Dongyeop and
Jurgens, David",
booktitle = "Proceedings of the 1st Workshop on Customizable NLP: Progress and Challenges in Customizing NLP for a Domain, Application, Group, or Individual (CustomNLP4U)",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.customnlp4u-1.5",
pages = "47--52",
abstract = "This pilot study explores the application of language models (LMs) to model game event sequences, treating them as a customized natural language. We investigate a popular mobile game, transforming raw event data into textual sequences and pretraining a Longformer model on this data. Our approach captures the rich and nuanced interactions within game sessions, effectively identifying meaningful player segments. The results demonstrate the potential of self-supervised LMs in enhancing game design and personalization without relying on ground-truth labels.",
}
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%0 Conference Proceedings
%T Understanding Players as if They Are Talking to the Game in a Customized Language: A Pilot Study
%A Wang, Tianze
%A Honarijahromi, Maryam
%A Katsarou, Styliani
%A Mikheeva, Olga
%A Panagiotakopoulos, Theodoros
%A Smirnov, Oleg
%A Cao, Lele
%A Asadi, Sahar
%Y Kumar, Sachin
%Y Balachandran, Vidhisha
%Y Park, Chan Young
%Y Shi, Weijia
%Y Hayati, Shirley Anugrah
%Y Tsvetkov, Yulia
%Y Smith, Noah
%Y Hajishirzi, Hannaneh
%Y Kang, Dongyeop
%Y Jurgens, David
%S Proceedings of the 1st Workshop on Customizable NLP: Progress and Challenges in Customizing NLP for a Domain, Application, Group, or Individual (CustomNLP4U)
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F wang-etal-2024-understanding
%X This pilot study explores the application of language models (LMs) to model game event sequences, treating them as a customized natural language. We investigate a popular mobile game, transforming raw event data into textual sequences and pretraining a Longformer model on this data. Our approach captures the rich and nuanced interactions within game sessions, effectively identifying meaningful player segments. The results demonstrate the potential of self-supervised LMs in enhancing game design and personalization without relying on ground-truth labels.
%U https://aclanthology.org/2024.customnlp4u-1.5
%P 47-52
Markdown (Informal)
[Understanding Players as if They Are Talking to the Game in a Customized Language: A Pilot Study](https://aclanthology.org/2024.customnlp4u-1.5) (Wang et al., CustomNLP4U 2024)
ACL
- Tianze Wang, Maryam Honarijahromi, Styliani Katsarou, Olga Mikheeva, Theodoros Panagiotakopoulos, Oleg Smirnov, Lele Cao, and Sahar Asadi. 2024. Understanding Players as if They Are Talking to the Game in a Customized Language: A Pilot Study. In Proceedings of the 1st Workshop on Customizable NLP: Progress and Challenges in Customizing NLP for a Domain, Application, Group, or Individual (CustomNLP4U), pages 47–52, Miami, Florida, USA. Association for Computational Linguistics.