Papers by Joni Dambre
Sign Language Recognition with Transformer Networks (2020.lrec-1)
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| Challenge: | Sign language recognition is a complex problem, supported by large video corpora . previous work has used feature extraction or end-to-end deep learning to speed annotation . |
| Approach: | They propose to use OpenPose for human keypoint estimation and Convolutional Neural Networks to extract sign language features from video corpora. |
| Outcome: | The proposed method outperforms the state-of-the-art on the Flemish Sign Language corpus. |
Explaining Character-Aware Neural Networks for Word-Level Prediction: Do They Discover Linguistic Rules? (D18-1)
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| Challenge: | Character-level features are used in many natural language processing algorithms but little is known about the character-level patterns they learn. |
| Approach: | They extend contextual decomposition technique to convolutional neural networks and bidirectional long-term memory networks to evaluate and compare these models for morphological tagging on three morphology-dependent languages. |
| Outcome: | The proposed models implicitly discover understandable linguistic rules for morphological tagging on three morphology-dependent languages. |
Human Alignment: How Much Do We Adapt to LLMs? (2025.acl-short)
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| Challenge: | Large Language Models (LLMs) are becoming a common part of our lives, yet few studies have examined how they influence our behavior. |
| Approach: | They propose a cooperative language game in which players aim to converge on a word and play a game in a group. |
| Outcome: | The proposed game shows that humans notice and adapt to differences regardless of whether they are aware they are interacting with an LLM. |