Papers by Luna De Bruyne
In Benchmarks We Trust ... Or Not? (2025.emnlp-main)
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Ine Gevers, Victor De Marez, Jens Van Nooten, Jens Lemmens, Andriy Kosar, Ehsan Lotfi, Nikolay Banar, Pieter Fivez, Luna De Bruyne, Walter Daelemans
| Challenge: | Existing benchmarks for Large Language Models (LLMs) are inadequate and lack a clear solution. |
| Approach: | They propose checklists to cover all aspects of benchmarking issues, both for benchmark creation and usage. |
| Outcome: | The proposed checklists cover all aspects of benchmarking issues, both for benchmark creation and usage. |
Aspect-Based Emotion Analysis and Multimodal Coreference: A Case Study of Customer Comments on Adidas Instagram Posts (2022.lrec-1)
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| Challenge: | Aspect-based sentiment analysis of user-generated content has been relatively unexplored in recent years. |
| Approach: | They present a multimodal dataset for Aspect-Based Emotion Analysis (ABEA) they take the first steps in investigating the utility of multimodal coreference resolution in an ABEA framework. |
| Outcome: | The proposed dataset consists of 4,900 comments on 175 images and is annotated with aspect and emotion categories and the emotional dimensions of valence and arousal. |
An Emotional Mess! Deciding on a Framework for Building a Dutch Emotion-Annotated Corpus (2020.lrec-1)
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| Challenge: | Existing frameworks for emotion recognition are limited and do not allow for categorical versus dimensional oppositions. |
| Approach: | They propose to use the emotions joy, love, anger, sadness and fear as well as dimensional models to annotate texts from different domains and topics. |
| Outcome: | The proposed frameworks are well-suited to annotate texts from different domains and topics, but the connotation of the labels strongly depends on the origin of the texts. |
Misery Loves Complexity: Exploring Linguistic Complexity in the Context of Emotion Detection (2023.findings-emnlp)
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| Challenge: | a negative emotion is a cognitive bias that affects how we express thoughts and opinions online . a recent study shows that negative words generate more engagement and clicks than positive ones . |
| Approach: | They propose to use readability and linguistic complexity metrics to better understand emotions . they propose to fine-tune three state-of-the-art transformers to detect emotions based on a dataset . |
| Outcome: | The proposed model fails to predict emotions on complex texts, the authors show . they also show that more advanced models fail to predict complex texts . |