20 citations · 49 across the 34 of their papers we have counts for
7 papers · 1 filter
Reusing Latent Speech Representations for Query-Conditioned Topic Localization in Transcripts
Steffen Freisinger, Philipp Seeberger, Thomas Ranzenberger +2
Long transcripts are costly inputs for downstream NLP systems and often contain irrelevant context. We study query-conditioned topic localization: predicting the sentence span in a…
Evaluation Pitfalls and Challenges in Multimedia Event Extraction
Philipp Seeberger, Steffen Freisinger, Tobias Bocklet +1
Multimedia event extraction aims to jointly identify events and their arguments across multiple modalities, such as text and images, to support more comprehensive event understandi…
Reading Between the Waves: Robust Topic Segmentation Using Inter-Sentence Audio Features
Steffen Freisinger, Philipp Seeberger, Tobias Bocklet +1
Spoken content, such as online videos and podcasts, often spans multiple topics, which makes automatic topic segmentation essential for user navigation and downstream applications.…
Towards Multi-Level Transcript Segmentation: LoRA Fine-Tuning for Table-of-Contents Generation
Steffen Freisinger, Philipp Seeberger, Thomas Ranzenberger +2
Segmenting speech transcripts into thematic sections benefits both downstream processing and users who depend on written text for accessibility. We introduce a novel approach to hi…
Generalizing to Unseen Disaster Events: A Causal View
Philipp Seeberger, Steffen Freisinger, Tobias Bocklet +1
Due to the rapid growth of social media platforms, these tools have become essential for monitoring information during ongoing disaster events. However, extracting valuable insight…
SafeRoute: Adaptive Model Selection for Efficient and Accurate Safety Guardrails in Large Language Models
Seanie Lee, Dong Bok Lee, Dominik Wagner +5
Deploying large language models (LLMs) in real-world applications requires robust safety guard models to detect and block harmful user prompts. While large safety guard models achi…
Information Type Classification with Contrastive Task-Specialized Sentence Encoders
Philipp Seeberger, Tobias Bocklet, Korbinian Riedhammer
User-generated information content has become an important information source in crisis situations. However, classification models suffer from noise and event-related biases which…