UAlberta at SemEval-2026 Task 2: Temporal Fusion Models for Predicting Affect Over Time

Duc Ho; Khanh Bui; Daniela Teodorescu; Grzegorz Kondrak

UAlberta at SemEval-2026 Task 2: Temporal Fusion Models for Predicting Affect Over Time

Duc Ho, Khanh Bui, Daniela Teodorescu, Grzegorz Kondrak

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Abstract

We describe our systems for the SemEval 2026 Task 2 on Predicting Variation in Emotional Valence and Arousal from Ecological Essays. To predict affect in a single instance, and for forecasting dispositional change, we use embeddings from a language model and a Recurrent Neural Network. To predict state changes from a previous timestep to the next, we use time-series forecasting. Our systems ranked first for forecasting dispositional change, and third for forecasting state change over time. We make our code publicly available.

Anthology ID:: 2026.semeval-1.87
Volume:: Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
Month:: July
Year:: 2026
Address:: San Diego, California, USA
Editors:: Ekaterina Kochmar, Debanjan Ghosh, Kai North, Mamoru Komachi
Venues:: SemEval | WS
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 605–611
Language:
URL:: https://aclanthology.org/2026.semeval-1.87/
DOI:
Bibkey:
Cite (ACL):: Duc Ho, Khanh Bui, Daniela Teodorescu, and Grzegorz Kondrak. 2026. UAlberta at SemEval-2026 Task 2: Temporal Fusion Models for Predicting Affect Over Time. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 605–611, San Diego, California, USA. Association for Computational Linguistics.
Cite (Informal):: UAlberta at SemEval-2026 Task 2: Temporal Fusion Models for Predicting Affect Over Time (Ho et al., SemEval 2026)
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PDF:: https://aclanthology.org/2026.semeval-1.87.pdf

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@inproceedings{ho-etal-2026-ualberta,
    title = "{UA}lberta at {S}em{E}val-2026 Task 2: Temporal Fusion Models for Predicting Affect Over Time",
    author = "Ho, Duc  and
      Bui, Khanh  and
      Teodorescu, Daniela  and
      Kondrak, Grzegorz",
    editor = "Kochmar, Ekaterina  and
      Ghosh, Debanjan  and
      North, Kai  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.semeval-1.87/",
    pages = "605--611",
    ISBN = "979-8-89176-414-9",
    abstract = "We describe our systems for the SemEval 2026 Task 2 on Predicting Variation in Emotional Valence and Arousal from Ecological Essays. To predict affect in a single instance, and for forecasting dispositional change, we use embeddings from a language model and a Recurrent Neural Network. To predict state changes from a previous timestep to the next, we use time-series forecasting. Our systems ranked first for forecasting dispositional change, and third for forecasting state change over time. We make our code publicly available."
}

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%0 Conference Proceedings
%T UAlberta at SemEval-2026 Task 2: Temporal Fusion Models for Predicting Affect Over Time
%A Ho, Duc
%A Bui, Khanh
%A Teodorescu, Daniela
%A Kondrak, Grzegorz
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F ho-etal-2026-ualberta
%X We describe our systems for the SemEval 2026 Task 2 on Predicting Variation in Emotional Valence and Arousal from Ecological Essays. To predict affect in a single instance, and for forecasting dispositional change, we use embeddings from a language model and a Recurrent Neural Network. To predict state changes from a previous timestep to the next, we use time-series forecasting. Our systems ranked first for forecasting dispositional change, and third for forecasting state change over time. We make our code publicly available.
%U https://aclanthology.org/2026.semeval-1.87/
%P 605-611

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Markdown (Informal)

[UAlberta at SemEval-2026 Task 2: Temporal Fusion Models for Predicting Affect Over Time](https://aclanthology.org/2026.semeval-1.87/) (Ho et al., SemEval 2026)

UAlberta at SemEval-2026 Task 2: Temporal Fusion Models for Predicting Affect Over Time (Ho et al., SemEval 2026)

ACL

Duc Ho, Khanh Bui, Daniela Teodorescu, and Grzegorz Kondrak. 2026. UAlberta at SemEval-2026 Task 2: Temporal Fusion Models for Predicting Affect Over Time. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 605–611, San Diego, California, USA. Association for Computational Linguistics.