Papers by Emile Chapuis
Code-switched inspired losses for spoken dialog representations (2021.emnlp-main)
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| Challenge: | We introduce new pretraining losses tailored to learn generic multilingual spoken dialogue representations . goal is to expose model to code-switched language . |
| Approach: | They propose to build a pretraining corpus of multilingual conversations in five different languages from OpenSubtitles. |
| Outcome: | The proposed models perform better in monolingual and multilingual settings. |
Hierarchical Pre-training for Sequence Labelling in Spoken Dialog (2020.findings-emnlp)
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| Challenge: | Sequence labelling tasks like Dialog Act and Emotion/Sentiment identification are a key component of spoken dialog systems. |
| Approach: | They propose a new approach to learn generic representations adapted to spoken dialog using a hierarchical encoder based on transformer architectures. |
| Outcome: | The proposed model is model-agnostic and contains 10 datasets of various sizes. |
Improving Multimodal fusion via Mutual Dependency Maximisation (2021.emnlp-main)
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| Challenge: | Multimodal sentiment analysis is a trending area of research, and multimodal fusion is one of its most active topics. |
| Approach: | They propose to use modality-based penalties to measure dependency between models to improve accuracy. |
| Outcome: | The proposed methods improve accuracy on two well-known sentiment analysis datasets by 4.3 on the proposed models and by-product includes a statistical network which can interpret the high dimensional representations learnt by the model. |