Papers by Philipp Cimiano
Recent Developments for the Linguistic Linked Open Data Infrastructure (2020.lrec-1)
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Thierry Declerck, John Philip McCrae, Matthias Hartung, Jorge Gracia, Christian Chiarcos, Elena Montiel-Ponsoda, Philipp Cimiano, Artem Revenko, Roser Saurí, Deirdre Lee, Stefania Racioppa, Jamal Abdul Nasir, Matthias Orlikowsk, Marta Lanau-Coronas, Christian Fäth, Mariano Rico, Mohammad Fazleh Elahi, Maria Khvalchik, Meritxell Gonzalez, Katharine Cooney
| Challenge: | Language data is rarely 'ready-to-use' and language technology specialists spend over 80% of their time cleaning, organizing and collecting language datasets. |
| Approach: | They propose a methodology for building data value chains based around language resources and language technologies that can be integrated by means of semantic technologies. |
| Outcome: | The proposed methodology is based on language resources and language technologies that can be integrated by means of semantic technologies. |
From Argumentation to Deliberation: Perspectivized Stance Vectors for Fine-grained (Dis)agreement Analysis (2025.findings-naacl)
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| Challenge: | Existing methods to identify conflict resolution points require a deeper analysis of arguments and the perspectives they are grounded in. |
| Approach: | They propose a framework for a deliberative analysis of arguments in a computational argumentation setup. |
| Outcome: | The proposed framework allows us to identify actionable options for conflict resolution, as a first step towards deliberation. |
BiQuAD: Towards QA based on deeper text understanding (2021.starsem-1)
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| Challenge: | Recent question answering and machine reading benchmarks require systems to pinpoint the span of the answer to a given text. |
| Approach: | They propose a dataset that requires deeper comprehension to answer questions extractively and deductively. |
| Outcome: | The proposed dataset outperforms existing benchmarks on extractive and deductive questions. |
Modeling the Quality of Dialogical Explanations (2024.lrec-main)
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Milad Alshomary, Felix Lange, Meisam Booshehri, Meghdut Sengupta, Philipp Cimiano, Henning Wachsmuth
| Challenge: | Existing studies have focused on the interaction of explanation moves, dialogue acts, and topics in successful dialogues with expert explainers. |
| Approach: | They construct a corpus of 399 reddit dialogues and analyze interaction flows and explainee quality using two language models that can handle long inputs. |
| Outcome: | The proposed model predicts that the interaction flows between the explainer and the explainee correlate with the quality of the explanations in terms of a successful understanding on the explain's side. |
Pointing Out the Shortcomings of Relation Extraction Models with Semantically Motivated Adversarials (2024.lrec-main)
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| Challenge: | Recent large language models have achieved state-of-the-art performance on many NLP tasks, but they rely on shortcut features and are unreliable when put under pressure. |
| Approach: | They propose to use semantically-motivated strategies to generate adversarial examples by replacing entity mentions to generate relation extraction models. |
| Outcome: | The proposed models show a lack of robustness when put under pressure. |
Zero-Shot Cross-Lingual Opinion Target Extraction (N19-1)
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| Challenge: | Aspect-based sentiment analysis involves the recognition of opinion target expressions . supervised learning algorithms are usually employed to extract OTEs from text . |
| Approach: | They propose a zero-shot cross-lingual approach for the extraction of opinion target expressions . they leverage multilingual word embeddings that share a common vector space across languages . |
| Outcome: | The proposed approach can perform accurate prediction on a target language without using annotated samples. |
The Ecological Fallacy in Annotation: Modeling Human Label Variation goes beyond Sociodemographics (2023.acl-short)
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| Challenge: | Existing work has attempted to model individual annotation behaviour rather than predicting aggregated labels. |
| Approach: | They propose to model individual annotator behaviour rather than predicting aggregated labels by adding group-specific layers to multi-annotator models to account for sociodemographics. |
| Outcome: | The proposed model does not significantly improve on toxic content detection tasks. |
Architectural Sweet Spots for Modeling Human Label Variation by the Example of Argument Quality: It’s Best to Relate Perspectives! (2023.emnlp-main)
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| Challenge: | Existing approaches to subjectivity in natural language processing are subjective . authors argue that disagreement should not be regarded as a problem . |
| Approach: | They propose to account for subjective perspectives of individuals and objective concepts that build a common ground between annotators. |
| Outcome: | The proposed architectures increase the averaged annotator-individual F1-scores up to 43% over a majority-label model. |
“Tell me who you are and I tell you how you argue”: Predicting Stances and Arguments for Stakeholder Groups (2024.findings-naacl)
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| Challenge: | Argument mining has focused on the identification, extraction, and formalization of arguments. |
| Approach: | They propose a framework that relies on a recommender-based architecture to predict stances and argumentative main points on societally controversial topics for a given stakeholder. |
| Outcome: | The proposed framework predicts arguments on a debate topic based on BERTScore and debate.org datasets. |
SANTO: A Web-based Annotation Tool for Ontology-driven Slot Filling (P18-4)
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| Challenge: | SANTO is an annotation tool designed for complex relation extraction tasks . a subset of information extraction tasks can be typed n-ary relation extraction or slot filling . |
| Approach: | They propose a domain-adaptive annotation tool for complex slot filling tasks . SANTO enables fast and clearly structured annotation for multiple users in parallel . |
| Outcome: | The proposed tool can be used for slot filling tasks and import and export procedures of standard formats enable interoperability with external sources and tools. |
Similarity-weighted Construction of Contextualized Commonsense Knowledge Graphs for Knowledge-intense Argumentation Tasks (2023.acl-long)
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| Challenge: | Arguments often do not make explicit how a conclusion follows from its premises . we present a method for constructing Contextualized Commonsense Knowledge Graphs (CCKGs) that is efficient and high-quality . |
| Approach: | They propose an unsupervised method for constructing Contextualized Commonsense Knowledge Graphs (CCKGs) they use triplet similarities to extract contextually relevant knowledge paths . |
| Outcome: | The proposed method outperforms baselines and a GPT-3 based system in a knowledge-intense argumentation task. |
Argument Summarization and its Evaluation in the Era of Large Language Models (2025.emnlp-main)
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Moritz Altemeyer, Steffen Eger, Johannes Daxenberger, Yanran Chen, Tim Altendorf, Philipp Cimiano, Benjamin Schiller
| Challenge: | Large Language Models (LLMs) have revolutionized various Natural Language Generation tasks, including Argument Summarization (ArgSum). |
| Approach: | They propose a prompt-based evaluation scheme and validate it through a human benchmark dataset. |
| Outcome: | The proposed evaluation scheme outperforms existing methods and is validated by a human benchmark dataset. |
Beyond Demographics: Fine-tuning Large Language Models to Predict Individuals’ Subjective Text Perceptions (2025.acl-long)
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| Challenge: | Recent work has shown that LLMs perform poorly when prompted with sociodemographic attributes, suggesting limited inherent sociodemography knowledge. |
| Approach: | They propose to train large language models to be accurate sociodemographic models of annotator variation by using a curated dataset of five tasks with standardized sociodemography. |
| Outcome: | The proposed models improve in sociodemographic prompting when trained but this performance gain is largely due to models learning annotator-specific behaviour rather than sociodemography. |