Four papers accepted at LREC-COLING 2024!

What a great start of the year! We are very excited to announce that we got four paper accepted at LREC-COLING 2024.

“Appraisal Framework for Clinical Empathy: A Novel Application to Breaking Bad News Conversations”

Authors: Allison Lahnala, Béla Neuendorf, Alexander Thomin, Charles Welch, Tina Stibane,Lucie Flek

Camera-Ready: Paper

In this paper, we introduce an annotation approach that draws on well-established frameworks for clinical empathy and breaking bad news (BBN) conversations for considering the interactive dynamics of discourse relations. We construct Empathy in BBNs, a span-relation task dataset of simulated BBN conversations in German, using our annotation scheme, in collaboration with a large medical school to support research on educational tools for medical didactics. The annotation is based on 1) appraisal framework for clinical empathy, which is grounded in systemic functional linguistics, and 2) the SPIKES protocol for breaking bad news, commonly taught in medical didactics training. This approach presents novel opportunities to study clinical empathic behavior and enables the training of models to detect causal relations involving empathy.

“LeadEmpathy: An Expert Annotated German Dataset of Empathy in Written Leadership Communication”

Authors: Didem Sedefoglu, Allison Lahnala, Jasmin Wagner, Lucie Flek, Sandra Ohly

Camera-Ready: Paper

Empathetic leadership communication plays a pivotal role in modern workplaces as it is associated with a wide range of positive individual and organizational outcomes. This paper introduces LeadEmpathy, an innovative expert-annotated German dataset for modeling empathy in written leadership communication. It features a novel theory-based coding scheme to model cognitive and affective empathy in asynchronous communication. The final dataset comprises 770 annotated emails from 385 participants who were allowed to rewrite their emails after receiving recommendations for increasing empathy in an online experiment.

“Reference-guided Style-Consistent Content Transfer”

Authors: Wei-Fan Chen, Milad Alshomary, Maja Stahl ,Khalid Al Khatib, Benno Stein, Henning Wachsmuth

Camera-Ready: Paper

This paper studies modifying a text’s content based on a reference while preserving its original style. We propose a multi-task learning model employed on hotel reviews and news article datasets. We contribute to provide a model integrating generation with faithfulness, style adherence, and coherence.

“DeFaktS: A German Dataset for Fine-Grained Disinformation Detection through Social Media Framing”

Authors: Shaina Ashraf, Isabel Bezzaoui, Ionut Andone, Alexander Markowetz, Jonas Fegert, Lucie Flek

Camera-Ready: Paper

This paper introduces a new dataset “DeFaktS”, designed to understand and counter disinformation within German media. Distinctively curated across various news topics, DeFaktS offers an unparalleled insight into the diverse facets of disinformation. A key attribute that sets DeFaktS apart is its fine-grained annotations based on polarized categories. Our annotation framework, grounded in the textual characteristics of disinformation content, eliminates the need for external knowledge sources. Unlike most existing corpora that typically assign a singular global veracity value to news, our methodology seeks to annotate every structural component and semantic element of a news piece, ensuring a comprehensive and detailed understanding.

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