Papers › Auxiliary Objectives for Neural Error Detection Models
Auxiliary Objectives for Neural Error Detection Models
Marek Rei, Helen Yannakoudakis
We investigate the utility of different auxiliary objectives and training strategies within a neural sequence labeling approach to error detection in learner writing. Auxiliary costs provide the model with additional linguistic information, allowing it to learn general-purpose compositional features that can then be exploited for other objectives. Our experiments show that a joint learning approach trained with parallel labels on in-domain data improves performance over the previous best error detection system. While the resulting model has the same number of parameters, the additional objectives allow it to be optimised more efficiently and achieve better performance.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Grammatical Error Detection | CoNLL-2014 A1 | Bi-LSTM + POS (unrestricted data) | F0.5 | 36.1 | #2 of 8 | Archive leaderboard | report |
| Grammatical Error Detection | CoNLL-2014 A1 | Bi-LSTM + POS (trained on FCE) | F0.5 | 17.5 | #7 of 8 | Archive leaderboard | report |
| Grammatical Error Detection | CoNLL-2014 A2 | Bi-LSTM + POS (unrestricted data) | F0.5 | 45.1 | #2 of 8 | Archive leaderboard | report |
| Grammatical Error Detection | CoNLL-2014 A2 | Bi-LSTM + POS (trained on FCE) | F0.5 | 26.2 | #6 of 8 | Archive leaderboard | report |
| Grammatical Error Detection | FCE | Bi-LSTM + err POS GR | F0.5 | 47.7 | #5 of 8 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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