xaver-maria-krueckl/situation-entity-segmenter

XLM-RoBERTa-large + CRF for Situation-Entity Segmentation

Paper | Code

Fine-tuned XLM-RoBERTa-large with a linear classifier and a CRF output layer for situation-entity segmentation.

The model assigns BI(O) tags (B-EDU, I-EDU) to each token, marking the boundaries and spans of situation-entity segments — contiguous clause-level segments that describe a single situation type.

We use the multilingual version of RoBERTa to improve possible zero-shot transfer to situation segmentation in other language varieties.

Usage

Requirements

pip install transformers torch pytorch-crf

spaCy is not a hard dependency, but is recommended for sentence splitting (matching the training setup):

pip install spacy && python -m spacy download en_core_web_sm

Loading the model

from transformers import AutoConfig, AutoModel, AutoTokenizer

config    = AutoConfig.from_pretrained("coling-unia/situation-entity-segmenter", trust_remote_code=True)
model     = AutoModel.from_pretrained("coling-unia/situation-entity-segmenter", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("FacebookAI/xlm-roberta-large")

model.eval()

Inference

The model was trained on spaCy-tokenised, sentence-split input (en_core_web_sm), so inference should follow the same setup. Split your input text into sentences using spaCy first, then call model.predict_text(words, tokenizer) with the word tokens for each sentence:

import spacy

nlp = spacy.load("en_core_web_sm")

text    = "The cat sat on the mat. It looked around the room."
results = []

for sent in nlp(text).sents:
    words = [token.text for token in sent]
    results.extend(model.predict_text(words, tokenizer))

for word, tag in results:
    print(f"{word:20s} {tag}")

B-EDU marks the start of a new situation-entity segment; I-EDU marks its continuation; O marks tokens outside any segment.

Architecture

XLM-RoBERTa-large encoder  →  Linear(1024 → 3)  →  CRF(3 tags)

• Encoder: FacebookAI/xlm-roberta-large
• Classifier: single linear layer mapping the encoder's hidden states to 3 tag logits
• Decoder: Viterbi decoding via a linear-chain CRF (pytorch-crf)
• Labels: B-EDU (0), I-EDU (1)

Training Data

Fine-tuned on the situation entity annotated corpus from:

Annemarie Friedrich, Alexis Palmer and Manfred Pinkal. Situation entity types: automatic classification of clause-level aspect. ACL 2016. (GitHub)

The dataset is licensed under the Apache License 2.0. Per the terms of the Apache 2.0 license, notice is hereby given that these weights represent a modified derivative work based on that data.

The corpus contains English text with clause-level situation-entity annotations. The standard train/dev/test split from the original paper is used.

Training Details

Hyperparameter	Value
Base model	FacebookAI/xlm-roberta-large
Learning rate	4e-5
Epochs (max)	20
Batch size	64
Weight decay	0.001
Early stopping	patience 3 (B-EDU F1 on dev)
Precision	fp16

Please find further training details in our code on GitHub.

Results

Evaluated on the held-out test set. The table shows the best single run and the mean ± std across 5 random seeds for the best hyperparameter configuration (lr=4e-5, wd=0.001). A full grid search over 4 configurations × 5 seeds (20 runs total) was conducted; all configurations achieved similar B-EDU F1 in the range 0.902–0.904.

Metric	Best run	Mean ± std (5 seeds)
B-EDU F1	0.907	0.904 ± 0.002
B-EDU Precision	0.901	0.898 ± 0.010
B-EDU Recall	0.914	0.911 ± 0.009
WindowDiff (↓)	0.075	0.077 ± 0.002
Exact Match (sentence)	0.753	0.742 ± 0.007

WindowDiff (Pevzner & Hearst, 2002) measures boundary-level segmentation quality within a sliding window of half the average reference segment length (lower is better). Exact Match is the fraction of sentences whose full tag sequence is predicted correctly (sentence level).

Limitations

• Trained and evaluated on ~40.000 situation English segments.
• Performance may vary on out-of-domain text.
• Sub-token sequences longer than 512 tokens need to be chunked before inference - regular sentences should be shorter, though.

Acknowledgement

We gratefully acknowledge the scientific support and HPC resources provided by the Erlangen National High Performance Computing Center (NHR@FAU) of the Friedrich-Alexander Universität Erlangen-Nürnberg (FAU) under the NHR project v110ee. NHR funding is provided by federal and Bavarian state authorities. NHR@FAU hardware is partially funded by the German Research Foundation (DFG) – 440719683.

Citation

Please cite our paper when using the model:

@inproceedings{schmuck-etal-2026-cross,
    title = "Cross-Linguistic Situation Entity Segmentation for Discourse Analysis in Diachronic {E}nglish and {G}erman Text",
    author = {Schm{\"u}ck, Hanna  and
      Urban, Veronika  and
      Kr{\"u}ckl, Xaver  and
      Zeman, Sonja  and
      Claridge, Claudia  and
      Friedrich, Annemarie},
    editor = "Liu, Yang Janet  and
      Gessler, Luke",
    booktitle = "Proceedings of the 20th Linguistic Annotation Workshop ({LAW} {XX})",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.law-main.8/",
    doi = "10.18653/v1/2026.law-main.8",
    pages = "95--112",
    ISBN = "979-8-89176-404-0",
    abstract = "Situation Entity (SE) segmentation identifies clause-like discourse units focusing on verb constellations. While SE segmentation has been applied to contemporary English as a subtask of SE annotation, systematic guidelines for syntactically ambiguous constructions remain underspecified. We present principled SE segmentation guidelines for contemporary and historical varieties of English and German. Our inter-annotator agreement studies on Late Modern English (1700{--}1900) and New High German (1650{--}1900) corpora demonstrate substantial agreement. Using the existing SitEnt corpus in contemporary English, we implement a new automatic segmenter based on XLM-RoBERTa. Our evaluation examines cross-variety and cross-lingual generalization, demonstrating challenges both for human annotation efforts and in transferring segmenters trained on contemporary English to historical varieties. Our code and data are publicly available at https://github.com/coling-unia/sitent-segmenter-law2026."
}

Please also cite the original annotation data paper:

@inproceedings{friedrich-etal-2016-situation,
    title = "Situation entity types: automatic classification of clause-level aspect",
    author = "Friedrich, Annemarie  and
      Palmer, Alexis  and
      Pinkal, Manfred",
    editor = "Erk, Katrin  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P16-1166/",
    doi = "10.18653/v1/P16-1166",
    pages = "1757--1768"
}

Cheers!