Papers by Cyril Goutte
Human or Neural Translation? (2020.coling-main)
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Shivendra Bhardwaj, David Alfonso Hermelo, Phillippe Langlais, Gabriel Bernier-Colborne, Cyril Goutte, Michel Simard
| Challenge: | a recent study shows that deep neural models have improved machine translation . identifying machine translation is still feasible, but is not yet known. |
| Approach: | They train and apply deep neural models to distinguish between human and machine translations . they use a monolingual and bilingual task to train and train 18 classifiers based on their results . |
| Outcome: | The proposed model improves the ability to distinguish between human and machine translations at the sentence level. |
EuroGames16: Evaluating Change Detection in Online Conversation (L18-1)
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| Challenge: | a new method for detecting salient changes from on-line conversations is needed . linguistic preprocessing and time series are used to build a time series . |
| Approach: | They propose a framework for detecting salient changes from on-line conversations . they use linguistic preprocessing to build a time series and change point detection algorithms to detect salient change. |
| Outcome: | The proposed method can detect salient changes in on-line conversations with high accuracy. |
Real-time Change Point Detection using On-line Topic Models (C18-1)
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| Challenge: | Existing methods for detecting events from publicly available data streams such as twitter have been used to model topics from large corpora. |
| Approach: | They propose to use on-line Latent Dirichlet Allocation to model topic shifts and on-lines change point detection algorithms to detect when significant changes occur. |
| Outcome: | The proposed algorithm yields F-scores up to 52% on the detection of real-life changes from social media data streams. |