klinger.bib
@inproceedings{Bagdon2024,
author = {Christopher Bagdon and Prathamesh Karmalkar and Harsha Gurulingappa and Roman Klinger},
title = {"You are an expert annotator": Automatic Best–Worst-Scaling Annotations for Emotion Intensity Modeling},
booktitle = {Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
year = {2024},
month = {June},
address = {Mexico City, Mexico},
publisher = {Association for Computational Linguistics},
note = {accepted},
internaltype = {conferenceproc},
url = {https://www.romanklinger.de/publications/BagdonNAACL2024.pdf}
}
@inproceedings{Wuehrl2024b,
title = {Understanding Fine-grained Distortions in Reports of
Scientific Findings},
author = {Amelie W\"uhrl and Dustin Wright and Roman Klinger
and Isabelle Augenstein},
booktitle = {Findings of the Association for Computational
Linguistics: ACL 2024},
month = august,
year = {2024},
address = {Bangkok, Thailand},
publisher = {Association for Computational Linguistics},
pdf = {https://arxiv.org/pdf/2402.12431.pdf},
archiveprefix = {arXiv},
eprint = {2402.12431},
internaltype = {conferenceproc}
}
@inproceedings{Wemmer2024,
author = {Eileen Wemmer and Sofie Labat and Roman Klinger},
title = {EmoProgress: Cumulated Emotion Progression Analysis in Dreams and Customer Service Dialogues},
booktitle = {Proceedings of the the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING)},
year = {2024},
address = {Turin, Italy},
internaltype = {conferenceproc},
note = {accepted},
url = {https://www.romanklinger.de/publications/WemmerLabatKlingerLRECCOLING2024.pdf}
}
@inproceedings{Velutharambath2024,
author = {Aswathy Velutharambath and Amelie W\"uhrl and Roman
Klinger},
title = {Can Factual Statements be Deceptive? The DeFaBel
Corpus of Belief-based Deception},
booktitle = {Proceedings of the the 2024 Joint International
Conference on Computational Linguistics, Language
Resources and Evaluation (LREC-COLING)},
year = {2024},
address = {Turin, Italy},
internaltype = {conferenceproc},
note = {accepted},
pdf = {https://www.romanklinger.de/publications/VelutharambathWuehrlKlinger-LREC-COLING2024.pdf},
eprint = {2403.10185},
archiveprefix = {arXiv},
primaryclass = {cs.CL}
}
@inproceedings{Barreiss20242,
author = {Barei\ss{}, Patrick and Klinger, Roman and Barnes,
Jeremy},
title = {English Prompts are Better for {NLI}-based Zero-Shot
Emotion Classification than Target-Language Prompts},
year = {2024},
isbn = {9798400701726},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3589335.3651902},
doi = {10.1145/3589335.3651902},
abstract = {Emotion classification in text is a challenging task
due to the processes involved when interpreting a
textual description of a potential emotion
stimulus. In addition, the set of emotion categories
is highly domain-specific. For instance, literature
analysis might require the use of aesthetic emotions
(e.g., finding something beautiful), and social
media analysis could benefit from fine-grained sets
(e.g., separating anger from annoyance) than only
those that represent basic categories as they have
been proposed by Paul Ekman (anger, disgust, fear,
joy, surprise, sadness). This renders the task an
interesting field for zero-shot classifications, in
which the label set is not known at model
development time. Unfortunately, most resources for
emotion analysis are English, and therefore, most
studies on emotion analysis have been performed in
English, including those that involve prompting
language models for text labels. This leaves us with
a research gap that we address in this paper: In
which language should we prompt for emotion labels
on non-English texts? This is particularly of
interest when we have access to a multilingual large
language model, because we could request labels with
English prompts even for non-English data. Our
experiments with natural language inference-based
language models show that it is consistently better
to use English prompts even if the data is in a
different language.},
booktitle = {Companion Proceedings of the ACM on Web Conference
2024},
pages = {1318–1326},
numpages = {9},
location = {Singapore, Singapore},
series = {WWW '24},
internaltype = {workshop}
}
@inproceedings{wegge-klinger-2024-topic,
title = {Topic Bias in Emotion Classification},
author = {Wegge, Maximilian and Klinger, Roman},
editor = {van der Goot, Rob and Bak, JinYeong and
M{\"u}ller-Eberstein, Max and Xu, Wei and Ritter,
Alan and Baldwin, Tim},
booktitle = {Proceedings of the Ninth Workshop on Noisy and
User-generated Text (W-NUT 2024)},
month = mar,
year = {2024},
address = {San {\.G}iljan, Malta},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2024.wnut-1.9},
pages = {89--103},
abstract = {Emotion corpora are typically sampled based on
keyword/hashtag search or by asking study
participants to generate textual instances. In any
case, these corpora are not uniform samples
representing the entirety of a domain. We
hypothesize that this practice of data acquision
leads to unrealistic correlations between
overrepresented topics in these corpora that harm
the generalizability of models. Such topic bias
could lead to wrong predictions for instances like
{``}I organized the service for my aunt{'}s
funeral.{''} when funeral events are overpresented
for instances labeled with sadness, despite the
emotion of pride being more appropriate here. In
this paper, we study this topic bias both from the
data and the modeling perspective. We first label a
set of emotion corpora automatically via topic
modeling and show that emotions in fact correlate
with specific topics. Further, we see that emotion
classifiers are confounded by such topics. Finally,
we show that the established debiasing method of
adversarial correction via gradient reversal
mitigates the issue. Our work points out issues with
existing emotion corpora and that more
representative resources are required for fair
evaluation of models predicting affective concepts
from text.},
internaltype = {workshop},
pdf = {https://www.romanklinger.de/publications/WeggeKlinger2024.pdf},
eprint = {2312.09043},
archiveprefix = {arXiv},
primaryclass = {cs.CL}
}
@inproceedings{wuehrl-etal-2024-makes,
title = {What Makes Medical Claims (Un)Verifiable? Analyzing
Entity and Relation Properties for Fact
Verification},
author = {Wührl, Amelie and Menchaca Resendiz, Yarik and
Grimminger, Lara and Klinger, Roman},
editor = {Graham, Yvette and Purver, Matthew},
booktitle = {Proceedings of the 18th Conference of the European
Chapter of the Association for Computational
Linguistics (Volume 1: Long Papers)},
month = mar,
year = {2024},
address = {St. Julian{'}s, Malta},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2024.eacl-long.124},
pages = {2046--2058},
abstract = {Verifying biomedical claims fails if no evidence can
be discovered. In these cases, the fact-checking
verdict remains unknown and the claim is
unverifiable. To improve this situation, we have to
understand if there are any claim properties that
impact its verifiability. In this work we assume
that entities and relations define the core
variables in a biomedical claim{'}s anatomy and
analyze if their properties help us to differentiate
verifiable from unverifiable claims. In a study with
trained annotation experts we prompt them to find
evidence for biomedical claims, and observe how they
refine search queries for their evidence
search. This leads to the first corpus for
scientific fact verification annotated with
subject{--}relation{--}object triplets, evidence
documents, and fact-checking verdicts (the BEAR-FACT
corpus). We find (1) that discovering evidence for
negated claims (e.g., X{--}does-not-cause{--}Y) is
particularly challenging. Further, we see that
annotators process queries mostly by adding
constraints to the search and by normalizing
entities to canonical names. (2) We compare our
in-house annotations with a small crowdsourcing
setting where we employ both medical experts and
laypeople. We find that domain expertise does not
have a substantial effect on the reliability of
annotations. Finally, (3), we demonstrate that it is
possible to reliably estimate the success of
evidence retrieval purely from the claim text
(.82F$_1$), whereas identifying unverifiable claims
proves more challenging (.27F$_1$)},
pdf = {https://www.romanklinger.de/publications/Wuehrl-etal-2024-EACL.pdf},
eprint = {2402.01360},
archiveprefix = {arXiv},
primaryclass = {cs.CL},
internaltype = {conferenceproc}
}