Papers by Simon Flachs

3 papers
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.
Historical Text Normalization with Delayed Rewards (P19-1)

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Challenge: Recent work on a novel approach to historical text normalization has shown that policy gradient fine-tuning improves accuracy across languages.
Approach: They propose to train sequence-to-sequence models with simple token-level log-likelihood with reinforcement learning to optimize for exact matches.
Outcome: The proposed model outperforms phrase-based models in the evaluation metric for historical text normalization across languages.

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