klinger.bib
@proceedings{wassa-2026-1,
title = {The Proceedings for the 15th Workshop on
Computational Approaches to Subjectivity, Sentiment
Social Media Analysis ({WASSA} 2026)},
editor = {Barnes, Jeremy and Barriere, Valentin and De Clercq,
Orph{\'e}e and Klinger, Roman and Nouri, C{\'e}lia
and Nozza, Debora and Singh, Pranaydeep},
month = mar,
year = {2026},
address = {Rabat, Morocco},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2026.wassa-1.0/},
isbn = {979-8-89176-378-4},
internaltype = {edited}
}
@inproceedings{Greschner2026,
author = {Lynn Greschner and Sabine Weber and Roman Klinger},
title = {Trust Me, I Can Convince You: The Contextualized
Argument Appraisal Framework and the ContArgA
Corpus},
booktitle = {Proceedings of the Language Resources and Evaluation
Conference},
month = {May},
year = {2026},
address = {Palma de Mallorca, Spain},
publisher = {European Language Resources Association},
internaltype = {conferenceproc},
eprint = {2509.17844},
archiveprefix = {arXiv},
primaryclass = {cs.CL},
url = {https://arxiv.org/abs/2509.17844},
pdf = {https://www.romanklinger.de/publications/GreschnerWeberKlinger2026LREC.pdf}
}
@inproceedings{MenchacaResendiz2026,
author = {Menchaca Resendiz, Yarik and Roman Klinger},
title = {PARL: Prompt-based Agents for Reinforcement
Learning},
booktitle = {Proceedings of the Language Resources and Evaluation
Conference},
month = {May},
year = {2026},
address = {Palma de Mallorca, Spain},
publisher = {European Language Resources Association},
internaltype = {conferenceproc},
eprint = {2510.21306},
archiveprefix = {arXiv},
primaryclass = {cs.CL},
url = {https://arxiv.org/abs/2510.21306},
note = {accepted},
pdf = {https://www.romanklinger.de/publications/MenchacaResendizKlinger2026LREC.pdf}
}
@inproceedings{Greschner2026b,
author = {Lynn Greschner and Meike Bauer and Sabine Weber and Roman
Klinger},
title = {Categorical Emotions or Appraisals – Which Emotion
Model Explains Argument Convincingness Better?},
booktitle = {Proceedings of the Language Resources and Evaluation
Conference},
month = {May},
year = {2026},
address = {Palma de Mallorca, Spain},
publisher = {European Language Resources Association},
internaltype = {conferenceproc},
eprint = {2511.07162},
archiveprefix = {arXiv},
primaryclass = {cs.CL},
url = {https://arxiv.org/abs/2511.07162},
pdf = {https://www.romanklinger.de/publications/GreschnerBauerWeberKlinger2026LREC.pdf},
note = {accepted}
}
@inproceedings{Ronningstad2026,
author = {Egil Rønningstad and Roman Klinger and Lilja Øvrelid
and Erik Velldal},
title = {Entity-Level Sentiment Analysis with Sentence
Relevance Detection},
booktitle = {Proceedings of the Language Resources and Evaluation
Conference},
month = {May},
year = {2026},
address = {Palma de Mallorca, Spain},
publisher = {European Language Resources Association},
internaltype = {conferenceproc},
note = {accepted},
pdf = {https://www.romanklinger.de/publications/RønningstadKlingerVelldalØvrelid2026LREC.pdf}
}
@inproceedings{Schaefer2026b,
author = {Johannes Schäfer and Roman Klinger},
title = {Disambiguation of Emotion Annotations by
Contextualizing Events in Plausible Narratives},
booktitle = {Proceedings of the Language Resources and Evaluation
Conference},
month = {May},
year = {2026},
address = {Palma de Mallorca, Spain},
publisher = {European Language Resources Association},
internaltype = {conferenceproc},
eprint = {2508.09954},
archiveprefix = {arXiv},
primaryclass = {cs.CL},
url = {https://arxiv.org/abs/2508.09954},
note = {accepted},
pdf = {https://www.romanklinger.de/publications/SchaeferKlinger2026LREC.pdf}
}
@inproceedings{Weber2026b,
author = {Sabine Weber and Lynn Greschner and Roman Klinger},
title = {Less is More? The Role of Demographic Author Information in Emotion Classification of
Ambiguous Text},
booktitle = {Proceedings of the Language Resources and Evaluation
Conference},
month = {May},
year = {2026},
address = {Palma de Mallorca, Spain},
publisher = {European Language Resources Association},
internaltype = {conferenceproc},
pdf = {https://www.romanklinger.de/publications/WeberGreschnerKlinger2026LREC.pdf},
note = {accepted}
}
@inproceedings{schafer-etal-2026-appraisal,
title = {Appraisal Trajectories in Narratives Reveal Distinct
Patterns of Emotion Evocation},
author = {Sch{\"a}fer, Johannes and Wagner, Janne and Klinger,
Roman},
editor = {Barnes, Jeremy and Barriere, Valentin and De Clercq,
Orph{\'e}e and Klinger, Roman and Nouri, C{\'e}lia
and Nozza, Debora and Singh, Pranaydeep},
booktitle = {The Proceedings for the 15th Workshop on
Computational Approaches to Subjectivity, Sentiment
Social Media Analysis ({WASSA} 2026)},
month = mar,
year = {2026},
address = {Rabat, Morocco},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2026.wassa-1.7/},
pages = {73--82},
isbn = {979-8-89176-378-4},
abstract = {Understanding emotion responses relies on
reconstructing how individuals appraise
events. While prior work has studied emotion
trajectories and inherent correlations with
appraisals, it has considered appraisals only in a
snapshot analysis. However, because appraisal is a
complex, sequential process, we argue that it should
be analyzed based on how it unfolds throughout a
narrative. In this study, we investigate whether
trajectories of appraisals are distinctive for
different emotions in five-event stories {--}
narratives where each of five sentences describes an
event. We employ zero-shot prompting with a large
language model to predict appraisals on
sub-sequences of a narrative. We find that this
approach is effective in identifying relevant
appraisals in narratives, without prior knowledge of
the evoked emotion, enabling a comprehensive
analysis of appraisal trajectories. Furthermore, we
are the first to quantitatively identify typical
patterns of appraisal trajectories that distinguish
emotions. For example, a rising trajectory for
self-responsibility indicates trust, while a falling
trajectory suggests anger.},
internaltype = {workshop},
pdf = {https://www.romanklinger.de/publications/SchaeferWagnerKlingerWASSA2026.pdf}
}
@inproceedings{weber-etal-2026-says,
title = {Says Who? Argument Convincingness and Reader Stance
Are Correlated with Perceived Author Personality},
author = {Weber, Sabine and Greschner, Lynn and Klinger,
Roman},
editor = {Barnes, Jeremy and Barriere, Valentin and De Clercq,
Orph{\'e}e and Klinger, Roman and Nouri, C{\'e}lia
and Nozza, Debora and Singh, Pranaydeep},
booktitle = {The Proceedings for the 15th Workshop on
Computational Approaches to Subjectivity, Sentiment
Social Media Analysis ({WASSA} 2026)},
month = mar,
year = {2026},
address = {Rabat, Morocco},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2026.wassa-1.20/},
pages = {265--277},
isbn = {979-8-89176-378-4},
abstract = {Alongside its literal meaning, text also carries
implicit social signals: information that is used by
the reader to assign the author of the text a
specific identity or make assumptions about the
author{'}s character. The reader creates a mental
image of the author which influences the
interpretation of the presented information. This is
especially relevant for argumentative text, where
the credibility of the information might depend on
who provides it. We therefore focus on the question:
How do readers of an argument imagine its author?
Using the ContArgA corpus, we study arguments
annotated for convincingness and perceived author
properties (level of education and Big Five
personality traits). We find that annotators
perceive an author to be similar to themselves when
they agree with the stance of the argument. We also
find that the envisioned personality traits and
education level of the author are statistically
significantly correlated with the argument{'}s
convincingness. We conduct experiments with four
generative LLMs and a RoBERTa-based regression model
showing that LLMs do not replicate the annotators
judgments. Argument convincingness can however
provide a useful signal for modeling perceived
author personality when it is explicitly used during
training.},
internaltype = {workshop},
note = {accepted},
pdf = {https://www.romanklinger.de/publications/WeberGreschnerKlinger_WASSA2026.pdf}
}
@inproceedings{chen-etal-2026-emotionally,
title = {Emotionally Charged, Logically Blurred: {AI}-driven
Emotional Framing Impairs Human Fallacy Detection},
author = {Chen, Yanran and Greschner, Lynn and Klinger, Roman
and Klenk, Michael and Eger, Steffen},
editor = {Demberg, Vera and Inui, Kentaro and Marquez,
Llu{\'i}s},
booktitle = {Proceedings of the 19th Conference of the {E}uropean
Chapter of the {A}ssociation for {C}omputational
{L}inguistics (Volume 1: Long Papers)},
month = mar,
year = {2026},
address = {Rabat, Morocco},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2026.eacl-long.316/},
pages = {6709--6732},
isbn = {979-8-89176-380-7},
abstract = {Logical fallacies are common in public communication
and can mislead audiences; fallacious arguments may
still appear convincing despite lacking soundness,
because convincingness is inherently subjective. We
present the first computational study of how
emotional framing interacts with fallacies and
convincingness, using large language models (LLMs)
to systematically change emotional appeals in
fallacious arguments. We benchmark eight LLMs on
injecting emotional appeal into fallacious arguments
while preserving their logical structures, then use
the best models to generate stimuli for a human
study. Our results show that LLM-driven emotional
framing reduces human fallacy detection in F1 by
14.5{\%} on average. Humans perform better in
fallacy detection when perceiving enjoyment than
fear or sadness, and these three emotions also
correlate with significantly higher convincingness
compared to neutral or other emotion states. Our
work has implications for AI-driven emotional
manipulation in the context of fallacious
argumentation.},
eprint = {2510.09695},
archiveprefix = {arXiv},
primaryclass = {cs.CL},
url = {https://arxiv.org/abs/2510.09695},
internaltype = {conferenceproc}
}
@article{velutharambath2026deceptiondetectedcrosslinguisticstudy,
title = {What if Deception Cannot be Detected? A
Cross-Linguistic Study on the Limits of Deception
Detection from Text},
author = {Aswathy Velutharambath and Kai
Sassenberg and Roman Klinger},
journal = {Computational Linguistics},
year = {2026},
note = {in print},
eprint = {2505.13147},
archiveprefix = {arXiv},
primaryclass = {cs.CL},
url = {https://arxiv.org/abs/2505.13147},
internaltype = {journal},
doi = {10.1162/COLI.a.614}
}