Papers by Gabriel Bernier-Colborne
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. |
HardEval: Focusing on Challenging Tokens to Assess Robustness of NER (2020.lrec-1)
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| Challenge: | Named entity recognition (NER) systems are often evaluated on human annotations . a new evaluation method focuses on subsets of tokens that represent specific sources of errors . |
| Approach: | They propose a method that focuses on subsets of tokens that represent specific sources of errors. |
| Outcome: | The proposed evaluation method focuses on subsets of tokens that represent specific sources of errors. |
Methods, Applications, and Directions of Learning-to-Rank in NLP Research (2024.findings-naacl)
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| Challenge: | Learning-to-rank (LTR) algorithms aim to order items according to some criteria. |
| Approach: | They focus on the formal background of LTR and the most widely-used supervised methods . they also discuss how large language models are changing the LTR landscape . |
| Outcome: | The proposed methods are used in natural language processing and information retrieval tasks. |