Contrastively Pre-trained Event Embeddings with Schema-free LLM Annotations

Frank Mtumbuka; Steven Schockaert

doi:10.63317/3sezhi63dcqv

Contrastively Pre-trained Event Embeddings with Schema-free LLM Annotations

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Abstract

Event extraction is a notoriously challenging problem, among others due to the scarcity of suitable training data. Moreover, event-centric knowledge bases are not available for most domains, making traditional distant supervision strategies difficult to implement. In this paper, we evaluate the potential of using LLM-generated annotations as an alternative distant supervision signal. Specifically, we create a synthetically labelled event extraction corpus, using an LLM to identify event triggers and arguments, and to provide corresponding free-text descriptions. We then pre-train event embedding models on this corpus using a contrastive loss, before fine-tuning them in the usual way. We empirically show the effectiveness of this approach.

Anthology ID:: 2026.lrec-1.591
Volume:: Proceedings of the Fifteenth Language Resources and Evaluation Conference
Month:: May
Year:: 2026
Address:: Palma de Mallorca, Spain
Editors:: Stelios Piperidis, Núria Bel, Henk van den Heuvel, Nancy Ide, Simon Krek, Antonio Toral
Venue:: LREC
SIG:
Publisher:: ELRA Language Resource Association
Note:
Pages:: 7457–7478
Language:
External URL:: https://lrec.elra.info/lrec2026-main-591
DOI:: 10.63317/3sezhi63dcqv
Bibkey:
Cite (ACL):: Frank Mtumbuka and Steven Schockaert. 2026. Contrastively Pre-trained Event Embeddings with Schema-free LLM Annotations. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 7457–7478, Palma de Mallorca, Spain. ELRA Language Resource Association.
Cite (Informal):: Contrastively Pre-trained Event Embeddings with Schema-free LLM Annotations (Mtumbuka & Schockaert, LREC 2026)
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@inproceedings{mtumbuka-schockaert-2026-contrastively,
    title = "Contrastively Pre-trained Event Embeddings with Schema-free {LLM} Annotations",
    author = "Mtumbuka, Frank  and
      Schockaert, Steven",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://aclanthology.org/2026.lrec-1.591/",
    doi = "10.63317/3sezhi63dcqv",
    pages = "7457--7478",
    abstract = "Event extraction is a notoriously challenging problem, among others due to the scarcity of suitable training data. Moreover, event-centric knowledge bases are not available for most domains, making traditional distant supervision strategies difficult to implement. In this paper, we evaluate the potential of using LLM-generated annotations as an alternative distant supervision signal. Specifically, we create a synthetically labelled event extraction corpus, using an LLM to identify event triggers and arguments, and to provide corresponding free-text descriptions. We then pre-train event embedding models on this corpus using a contrastive loss, before fine-tuning them in the usual way. We empirically show the effectiveness of this approach."
}

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%0 Conference Proceedings
%T Contrastively Pre-trained Event Embeddings with Schema-free LLM Annotations
%A Mtumbuka, Frank
%A Schockaert, Steven
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F mtumbuka-schockaert-2026-contrastively
%X Event extraction is a notoriously challenging problem, among others due to the scarcity of suitable training data. Moreover, event-centric knowledge bases are not available for most domains, making traditional distant supervision strategies difficult to implement. In this paper, we evaluate the potential of using LLM-generated annotations as an alternative distant supervision signal. Specifically, we create a synthetically labelled event extraction corpus, using an LLM to identify event triggers and arguments, and to provide corresponding free-text descriptions. We then pre-train event embedding models on this corpus using a contrastive loss, before fine-tuning them in the usual way. We empirically show the effectiveness of this approach.
%R 10.63317/3sezhi63dcqv
%U https://aclanthology.org/2026.lrec-1.591/
%U https://doi.org/10.63317/3sezhi63dcqv
%P 7457-7478

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Markdown (Informal)

[Contrastively Pre-trained Event Embeddings with Schema-free LLM Annotations](https://aclanthology.org/2026.lrec-1.591/) (Mtumbuka & Schockaert, LREC 2026)

Contrastively Pre-trained Event Embeddings with Schema-free LLM Annotations (Mtumbuka & Schockaert, LREC 2026)

ACL

Frank Mtumbuka and Steven Schockaert. 2026. Contrastively Pre-trained Event Embeddings with Schema-free LLM Annotations. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 7457–7478, Palma de Mallorca, Spain. ELRA Language Resource Association.