|Title:||CoNLL-2013 Shared Task: Grammatical Error Correction|
|Creators:||Ng Hwee Tou |
Wu Siew Mei
|NUS Contact:||NG HWEE TOU |
Wu Siew Mei
|Subject:||Natural Language Processing|
Natural Langauge Learning
Grammatical error correction
Grammatical error detection
CoNLL-2013 will continue the CoNLL tradition of having a high profile shared task in natural language processing. This year's shared task will be grammatical error correction. A participating system in this shared task is given short English texts written by non-native speakers of English. The system detects the grammatical errors present in the input texts, and returns the corrected texts.
This task has gained popularity recently with the organization of the HOO (Helping Our Own) shared tasks in 2011 and 2012. In the most recent HOO shared task in 2012, on error detection and correction of determiners and prepositions, 14 teams from around the world participated and 85 systems were submitted to the shared task.
The grammatical error correction task is impactful since it is estimated that hundreds of millions of people in the world are learning English and they benefit directly from an automated grammar checker. However, for many error types, current grammatical error correction methods do not achieve a high performance and thus more research is needed.
Instead of focusing on only determiner and preposition errors as in HOO 2012, the CoNLL-2013 shared task will include a more comprehensive list of error types, including determiner, preposition, noun number, verb form, and subject-verb agreement errors. Extending into more error types introduces the possibility of correcting multiple interacting errors. Examples of such interacting errors include determiner and noun number ('that cars' → 'that car' or 'those cars') and preposition and verb form ('an interest to study’ → ‘an interest in studying').
Participating teams will be provided with common training data in which grammatical errors have been annotated. Blind test data will be used to evaluate the outputs of the participating teams using a common scoring software and evaluation metric.
To download and reuse this dataset, please visit https://doi.org/10.25542/GVZ8-1MME.
|Citation:||When using this data, please cite the original publication and also the dataset.|
|License:||Please refer to instructions on https://doi.org/10.25542/GVZ8-1MME.|
|Appears in Collections:||Staff Dataset|
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|release2.3.1.tar.gz||To download and reuse this dataset, please refer to instructions on https://doi.org/10.25542/GVZ8-1MME.||1.07 MB||Unknown|
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