activity
20232026
most citedMultiClimate: Multimodal Stance Detection on Climate Change Videos

1 citations · 6 across the 30 of their papers we have counts for

collaborators

33 papers

cs.CL2026

Information Asymmetry across Language Varieties: A Case Study on Cantonese-Mandarin and Bavarian-German QA

Renhao Pei, Siyao Peng, Verena Blaschke +2

Large Language Models (LLMs) are becoming a common way for humans to seek knowledge, yet their coverage and reliability vary widely. Especially for local language varieties, there…

cs.CL2026

Indirect Question Answering in English, German and Bavarian: A Challenging Task for High- and Low-Resource Languages Alike

Miriam Winkler, Verena Blaschke, Barbara Plank

Indirectness is a common feature of daily communication, yet is underexplored in NLP research for both low-resource as well as high-resource languages. Indirect Question Answering…

cs.CL2026

Decoupling the Effect of Chain-of-Thought Reasoning: A Human Label Variation Perspective

Beiduo Chen, Tiancheng Hu, Caiqi Zhang +3

Reasoning-tuned LLMs utilizing long Chain-of-Thought (CoT) excel at single-answer tasks, yet their ability to model Human Label Variation--which requires capturing probabilistic am…

cs.CL2025

EVADE: LLM-Based Explanation Generation and Validation for Error Detection in NLI

Longfei Zuo, Barbara Plank, Siyao Peng

High-quality datasets are critical for training and evaluating reliable NLP models. In tasks like natural language inference (NLI), human label variation (HLV) arises when multiple…

cs.CL2025

BoN Appetit Team at LeWiDi-2025: Best-of-N Test-time Scaling Can Not Stomach Annotation Disagreements (Yet)

Tomas Ruiz, Siyao Peng, Barbara Plank +1

Test-time scaling is a family of techniques to improve LLM outputs at inference time by performing extra computation. To the best of our knowledge, test-time scaling has been limit…

cs.CL2025

Agree, Disagree, Explain: Decomposing Human Label Variation in NLI through the Lens of Explanations

Pingjun Hong, Beiduo Chen, Siyao Peng +3

Natural Language Inference (NLI) datasets often exhibit human label variation. To better understand these variations, explanation-based approaches analyze the underlying reasoning…

cs.CL2025

Standard-to-Dialect Transfer Trends Differ across Text and Speech: A Case Study on Intent and Topic Classification in German Dialects

Verena Blaschke, Miriam Winkler, Barbara Plank

Research on cross-dialectal transfer from a standard to a non-standard dialect variety has typically focused on text data. However, dialects are primarily spoken, and non-standard…

cs.CL2025

Make Every Letter Count: Building Dialect Variation Dictionaries from Monolingual Corpora

Robert Litschko, Verena Blaschke, Diana Burkhardt +2

Dialects exhibit a substantial degree of variation due to the lack of a standard orthography. At the same time, the ability of Large Language Models (LLMs) to process dialects rema…

cs.CL2025

Evaluating Large Language Models for Cross-Lingual Retrieval

Longfei Zuo, Pingjun Hong, Oliver Kraus +2

Multi-stage information retrieval (IR) has become a widely-adopted paradigm in search. While Large Language Models (LLMs) have been extensively evaluated as second-stage reranking…

cs.CL2025

Reason to Rote: Rethinking Memorization in Reasoning

Yupei Du, Philipp Mondorf, Silvia Casola +3

Large language models readily memorize arbitrary training instances, such as label noise, yet they perform strikingly well on reasoning tasks. In this work, we investigate how lang…

cs.CL2025★ 1 cited

A Multi-Dialectal Dataset for German Dialect ASR and Dialect-to-Standard Speech Translation

Verena Blaschke, Miriam Winkler, Constantin Förster +2

Although Germany has a diverse landscape of dialects, they are underrepresented in current automatic speech recognition (ASR) research. To enable studies of how robust models are t…

cs.CL2025

Threading the Needle: Reweaving Chain-of-Thought Reasoning to Explain Human Label Variation

Beiduo Chen, Yang Janet Liu, Anna Korhonen +1

The recent rise of reasoning-tuned Large Language Models (LLMs)--which generate chains of thought (CoTs) before giving the final answer--has attracted significant attention and off…

cs.CL2025

LiTEx: A Linguistic Taxonomy of Explanations for Understanding Within-Label Variation in Natural Language Inference

Pingjun Hong, Beiduo Chen, Siyao Peng +2

There is increasing evidence of Human Label Variation (HLV) in Natural Language Inference (NLI), where annotators assign different labels to the same premise-hypothesis pair. Howev…

cs.CL2025

What Media Frames Reveal About Stance: A Dataset and Study about Memes in Climate Change Discourse

Shijia Zhou, Siyao Peng, Simon M. Luebke +4

Media framing refers to the emphasis on specific aspects of perceived reality to shape how an issue is defined and understood. Its primary purpose is to shape public perceptions of…

cs.CL2025

Add Noise, Tasks, or Layers? MaiNLP at the VarDial 2025 Shared Task on Norwegian Dialectal Slot and Intent Detection

Verena Blaschke, Felicia Körner, Barbara Plank

Slot and intent detection (SID) is a classic natural language understanding task. Despite this, research has only more recently begun focusing on SID for dialectal and colloquial v…

cs.CL2025

Improving Dialectal Slot and Intent Detection with Auxiliary Tasks: A Multi-Dialectal Bavarian Case Study

Xaver Maria Krückl, Verena Blaschke, Barbara Plank

Reliable slot and intent detection (SID) is crucial in natural language understanding for applications like digital assistants. Encoder-only transformer models fine-tuned on high-r…

cs.CL2025

Cross-Dialect Information Retrieval: Information Access in Low-Resource and High-Variance Languages

Robert Litschko, Oliver Kraus, Verena Blaschke +1

A large amount of local and culture-specific knowledge (e.g., people, traditions, food) can only be found in documents written in dialects. While there has been extensive research…

cs.CL2024

Evaluating Pixel Language Models on Non-Standardized Languages

Alberto Muñoz-Ortiz, Verena Blaschke, Barbara Plank

We explore the potential of pixel-based models for transfer learning from standard languages to dialects. These models convert text into images that are divided into patches, enabl…

cs.CL2024★ 1 cited

A Rose by Any Other Name: LLM-Generated Explanations Are Good Proxies for Human Explanations to Collect Label Distributions on NLI

Beiduo Chen, Siyao Peng, Anna Korhonen +1

Disagreement in human labeling is ubiquitous, and can be captured in human judgment distributions (HJDs). Recent research has shown that explanations provide valuable information f…

cs.CL2024★ 1 cited

MultiClimate: Multimodal Stance Detection on Climate Change Videos

Jiawen Wang, Longfei Zuo, Siyao Peng +1

Climate change (CC) has attracted increasing attention in NLP in recent years. However, detecting the stance on CC in multimodal data is understudied and remains challenging due to…

cs.CL2024

Exploring Large Language Models for Product Attribute Value Identification

Kassem Sabeh, Mouna Kacimi, Johann Gamper +2

Product attribute value identification (PAVI) involves automatically identifying attributes and their values from product information, enabling features like product search, recomm…

cs.CL2024

An Empirical Comparison of Generative Approaches for Product Attribute-Value Identification

Kassem Sabeh, Robert Litschko, Mouna Kacimi +2

Product attributes are crucial for e-commerce platforms, supporting applications like search, recommendation, and question answering. The task of Product Attribute and Value Identi…

cs.CL2024★ 1 cited

CLIMATELI: Evaluating Entity Linking on Climate Change Data

Shijia Zhou, Siyao Peng, Barbara Plank

Climate Change (CC) is a pressing topic of global importance, attracting increasing attention across research fields, from social sciences to Natural Language Processing (NLP). CC…

cs.CL2024

"Seeing the Big through the Small": Can LLMs Approximate Human Judgment Distributions on NLI from a Few Explanations?

Beiduo Chen, Xinpeng Wang, Siyao Peng +3

Human label variation (HLV) is a valuable source of information that arises when multiple human annotators provide different labels for valid reasons. In Natural Language Inference…