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Roman Klinger

14 accepted papers

2025

Donate or Create? Comparing Data Collection Strategies for Emotion-labeled Multimodal Social Media Posts

ACL 2025long

Accurate modeling of subjective phenomena such as emotion expression requires data annotated with authors’ intentions. Commonly such data is collected by asking study participants to donate and label genuine content produced in the real world, or create content fitting particu- lar labels during the…

Cited by 0SourcePDFScholar
2025

Which Demographics do LLMs Default to During Annotation?

ACL 2025long

Demographics and cultural background of annotators influence the labels they assign in text annotation – for instance, an elderly woman might find it offensive to read a message addressed to a “bro”, but a male teenager might find it appropriate. It is therefore important to acknowledge label variat…

Cited by 0SourcePDFScholar
2024

Can Factual Statements Be Deceptive? The DeFaBel Corpus of Belief-based Deception

COLING 2024main

If a person firmly believes in a non-factual statement, such as “The Earth is flat”, and argues in its favor, there is no inherent intention to deceive. As the argumentation stems from genuine belief, it may be unlikely to exhibit the linguistic properties associated with deception or lying. This in…

Cited by 1SourcePDFScholar
2024

Dealing with Controversy: An Emotion and Coping Strategy Corpus Based on Role Playing

EMNLP 2024finding

There is a mismatch between psychological and computational studies on emotions. Psychological research aims at explaining and documenting internal mechanisms of these phenomena, while computational work often simplifies them into labels. Many emotion fundamentals remain under-explored in natural la…

Cited by 0SourcePDFScholar
2024

EmoProgress: Cumulated Emotion Progression Analysis in Dreams and Customer Service Dialogues

COLING 2024main

Emotion analysis often involves the categorization of isolated textual units, but these are parts of longer discourses, like dialogues or stories. This leads to two different established emotion classification setups: (1) Classification of a longer text into one or multiple emotion categories. (2) C…

Cited by 2SourcePDFScholar
2024

Understanding Fine-grained Distortions in Reports of Scientific Findings

ACL 2024findings

Distorted science communication harms individuals and society as it can lead to unhealthy behavior change and decrease trust in scientific institutions. Given the rapidly increasing volume of science communication in recent years, a fine-grained understanding of how findings from scientific publicat…

Cited by 2SourcePDFScholar
2024

“You are an expert annotator”: Automatic Best–Worst-Scaling Annotations for Emotion Intensity Modeling

NAACL 2024long

Labeling corpora constitutes a bottleneck to create models for new tasks or domains. Large language models mitigate the issue with automatic corpus labeling methods, particularly for categorical annotations. Some NLP tasks such as emotion intensity prediction, however, require text regression, but t…

Cited by 5SourcePDFScholar
2022

Embarrassingly Simple Performance Prediction for Abductive Natural Language Inference

NAACL 2022long

The task of natural language inference (NLI), to decide if a hypothesis entails or contradicts a premise, received considerable attention in recent years. All competitive systems build on top of contextualized representations and make use of transformer architectures for learning an NLI model. When…

2022

Natural Language Inference Prompts for Zero-shot Emotion Classification in Text across Corpora

COLING 2022main

Within textual emotion classification, the set of relevant labels depends on the domain and application scenario and might not be known at the time of model development. This conflicts with the classical paradigm of supervised learning in which the labels need to be predefined. A solution to obtain…

2020

Appraisal Theories for Emotion Classification in Text

COLING 2020main

Automatic emotion categorization has been predominantly formulated as text classification in which textual units are assigned to an emotion from a predefined inventory, for instance following the fundamental emotion classes proposed by Paul Ekman (fear, joy, anger, disgust, sadness, surprise) or Rob…

Cited by 69SourcePDFScholar
2020

Lost in Back-Translation: Emotion Preservation in Neural Machine Translation

COLING 2020main

Machine translation provides powerful methods to convert text between languages, and is therefore a technology enabling a multilingual world. An important part of communication, however, takes place at the non-propositional level (e.g., politeness, formality, emotions), and it is far from clear whet…