@inproceedings{culnan-etal-2022-exploring,
title = "Exploring transformers and time lag features for predicting changes in mood over time",
author = "Culnan, John and
Romero Diaz, Damian and
Bethard, Steven",
editor = "Zirikly, Ayah and
Atzil-Slonim, Dana and
Liakata, Maria and
Bedrick, Steven and
Desmet, Bart and
Ireland, Molly and
Lee, Andrew and
MacAvaney, Sean and
Purver, Matthew and
Resnik, Rebecca and
Yates, Andrew",
booktitle = "Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology",
month = jul,
year = "2022",
address = "Seattle, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.clpsych-1.21",
doi = "10.18653/v1/2022.clpsych-1.21",
pages = "226--231",
abstract = "This paper presents transformer-based models created for the CLPsych 2022 shared task. Using posts from Reddit users over a period of time, we aim to predict changes in mood from post to post. We test models that preserve timeline information through explicit ordering of posts as well as those that do not order posts but preserve features on the length of time between a user{'}s posts. We find that a model with temporal information may provide slight benefits over the same model without such information, although a RoBERTa transformer model provides enough information to make similar predictions without custom-encoded time information.",
}
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<abstract>This paper presents transformer-based models created for the CLPsych 2022 shared task. Using posts from Reddit users over a period of time, we aim to predict changes in mood from post to post. We test models that preserve timeline information through explicit ordering of posts as well as those that do not order posts but preserve features on the length of time between a user’s posts. We find that a model with temporal information may provide slight benefits over the same model without such information, although a RoBERTa transformer model provides enough information to make similar predictions without custom-encoded time information.</abstract>
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%0 Conference Proceedings
%T Exploring transformers and time lag features for predicting changes in mood over time
%A Culnan, John
%A Romero Diaz, Damian
%A Bethard, Steven
%Y Zirikly, Ayah
%Y Atzil-Slonim, Dana
%Y Liakata, Maria
%Y Bedrick, Steven
%Y Desmet, Bart
%Y Ireland, Molly
%Y Lee, Andrew
%Y MacAvaney, Sean
%Y Purver, Matthew
%Y Resnik, Rebecca
%Y Yates, Andrew
%S Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, USA
%F culnan-etal-2022-exploring
%X This paper presents transformer-based models created for the CLPsych 2022 shared task. Using posts from Reddit users over a period of time, we aim to predict changes in mood from post to post. We test models that preserve timeline information through explicit ordering of posts as well as those that do not order posts but preserve features on the length of time between a user’s posts. We find that a model with temporal information may provide slight benefits over the same model without such information, although a RoBERTa transformer model provides enough information to make similar predictions without custom-encoded time information.
%R 10.18653/v1/2022.clpsych-1.21
%U https://aclanthology.org/2022.clpsych-1.21
%U https://doi.org/10.18653/v1/2022.clpsych-1.21
%P 226-231
Markdown (Informal)
[Exploring transformers and time lag features for predicting changes in mood over time](https://aclanthology.org/2022.clpsych-1.21) (Culnan et al., CLPsych 2022)
ACL