Papers by Ophélie Lacroix
Grammatical Error Correction in Low Error Density Domains: A New Benchmark and Analyses (2020.emnlp-main)
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| Challenge: | CWEB is a new benchmark for grammatical error correction (GEC) systems . website data contains far fewer grammamatical errors than learner essays . |
| Approach: | They propose to broaden the target domain of grammatical error correction (GEC) systems . website data contains far fewer grammamatical errors than learner essays . |
| Outcome: | The proposed model can't rely on a strong internal language model in low error density domains. |
A Simple and Robust Approach to Detecting Subject-Verb Agreement Errors (N19-1)
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| Challenge: | a recent study shows that neural sequential labelers overfit their training data to detect SVA errors. |
| Approach: | They propose a simple protocol that generates a neural sequential labeler from silver standard data and gold standard data. |
| Outcome: | The proposed method leads to more robust detection of SVA errors on silver standard data and gold standard data. |
Automatically Selecting the Best Dependency Annotation Design with Dynamic Oracles (N18-2)
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| Challenge: | Multiple annotation conventions have been proposed for representing dependency structures. |
| Approach: | They propose to consider a set of syntactic references encoding alternative syntak representations to train a parser with a dynamic oracle. |
| Outcome: | The proposed approach can predict the best syntactic representation among all possible references. |
DDisCo: A Discourse Coherence Dataset for Danish (2022.lrec-1)
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| Challenge: | Discourse coherence models have been developed using randomly shuffled texts instead of highly edited and coherent data. |
| Approach: | They propose to annotate Danish Wikipedia and Reddit for discourse coherence using real-world text instead of artificially incoherent text for training and testing models. |
| Outcome: | The proposed model performs well on annotated texts from the Danish Wikipedia and Reddit dataset. |