---
title: "Contrastively Pre-trained Event Embeddings — Mtumbuka 2026"
description: "Event extraction models are hard to train well because annotated data is limited and noisy, and the event-focused knowledge bases needed for classic…"
authors: ["Frank Mtumbuka", "Steven Schockaert"]
year: 2026
venue: "Proceedings of the Fifteenth Language Resources and Evaluation Conference"
source: "https://aclanthology.org/2026.lrec-1.591/"
doi: "10.63317/3sezhi63dcqv"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/mtumbuka-2026-contrastively-pre-trained-event-embeddings/"
---
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# Contrastively Pre-trained Event Embeddings with Schema-free LLM Annotations

**Authors:** Frank Mtumbuka, [Steven Schockaert](https://md.research.elamur.ai/free/authors/steven-schockaert/) · **Year:** 2026 · **Venue:** Proceedings of the Fifteenth Language Resources and Evaluation Conference · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.lrec-1.591/) · [PDF](https://aclanthology.org/2026.lrec-1.591.pdf) · [DOI 10.63317/3sezhi63dcqv](https://doi.org/10.63317/3sezhi63dcqv) · [OpenAlex](https://openalex.org/W7160145149)

**Citations:** 0 citations (openalex-snapshot · 2026-10-14)

## Summary
Event extraction models are hard to train well because annotated data is limited and noisy, and the event-focused knowledge bases needed for classic distant supervision rarely exist. The authors test whether LLM output can stand in for that missing signal. They randomly sample sentences from the Gigaword news corpus and have GPT-4o mark event triggers and arguments, along with short natural-language descriptions of each, without imposing any predefined schema. The descriptions are embedded with LLM2Vec, and a BERT-style encoder is pre-trained with a contrastive objective so that its token representations of triggers and arguments match the embeddings of their descriptions. The resulting approach, called COPELLA, is then fine-tuned on standard labelled data. Across MAVEN-ARG, MAVEN and ACE-2005, the pre-trained encoders show consistent improvements. Annotations from the weaker GPT-4o-mini bring little benefit, which suggests that annotation quality is decisive. The work offers a practical way to strengthen small, efficient extractors using inexpensive, schema-independent LLM annotations.

## Key points
- Released resource: 150k Gigaword sentences annotated by GPT-4o, plus GPT-4o-mini annotations for a 100k subset. Code and data are at github.com/fmtumbuka/restore-event-extraction.
- Pre-training aligns encoder token embeddings with LLM2Vec (Llama-3-8B-Instruct) embeddings of the free-text descriptions. Several encoders were tested, including BERT-base, BERT-large and RoBERTa-large, with a contrastive temperature of 0.05 and a 5:1 negative ratio.
- With 100k pre-training sentences and RoBERTa-large, F1 on MAVEN is 74.9 using GPT-4o annotations versus 67.4 using GPT-4o-mini. On MAVEN-ARG the comparable figures are 42.3 versus 38.3.
- Larger pre-training sets help. On MAVEN with RoBERTa-large, F1 increases from about 72.3 at 25k sentences to 75.8 at 150k.
- Unlike earlier distant supervision based on knowledge bases or AMR, the method does not depend on a fixed event schema.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{mtumbuka2026contrastively,
  title = {{Contrastively Pre-trained Event Embeddings with Schema-free LLM Annotations}},
  author = {Mtumbuka, Frank and Schockaert, Steven},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/3sezhi63dcqv},
  url = {https://doi.org/10.63317/3sezhi63dcqv}
}
```

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