Publications (6)
Commute-Time-Optimised Graphs for GNNs
Igor Sterner, Shiye Su, Petar VeliÄkoviÄ
We explore graph rewiring methods that optimise commute time. Recent graph rewiring approaches facilitate long-range interactions in sparse graphs, making such rewirings commute-ti…
Contrastive Learning with Narrative Twins for Modeling Story Salience
Igor Sterner, Alex Lascarides, Frank Keller
Understanding narratives requires identifying which events are most salient for a story's progression. We present a contrastive learning framework for modeling narrative salience t…
Minimal Pair-Based Evaluation of Code-Switching
Igor Sterner, Simone Teufel
There is a lack of an evaluation methodology that estimates the extent to which large language models (LLMs) use code-switching (CS) in the same way as bilinguals. Existing methods…
Code-Switching and Syntax: A Large-Scale Experiment
Igor Sterner, Simone Teufel
The theoretical code-switching (CS) literature provides numerous pointwise investigations that aim to explain patterns in CS, i.e. why bilinguals switch language in certain positio…
Few-Shot VQA with Frozen LLMs: A Tale of Two Approaches
Igor Sterner, Weizhe Lin, Jinghong Chen +1
Two approaches have emerged to input images into large language models (LLMs). The first is to caption images into natural language. The second is to map image feature embeddings i…
Segment Any Text: A Universal Approach for Robust, Efficient and Adaptable Sentence Segmentation
Markus Frohmann, Igor Sterner, Ivan VuliÄ +2
Segmenting text into sentences plays an early and crucial role in many NLP systems. This is commonly achieved by using rule-based or statistical methods relying on lexical features…