Choosing the Right Word: Using Bidirectional LSTM Tagger for Writing Support Systems
Table 4: Lower bound (underestimate) MRR for the general-purpose test set. The MRR values for useful suggestions are indicated in bold.
['[BOLD] Error type', '[BOLD] Number of sentences in the test set', '[BOLD] MRR']
General purpose text test set. For a general purpose test set we use a subset of the CoNLL-2014 GEC shared task dataset ng-EtAl:2014:W14-17, that consists of 2421 sentences. The best results, in which the gold standard correction was suggested by our method was between second and fifth place are marked in bold. The preposition, pronoun form, and verb modal error types are handled especially well by our method, and the word replacement candidates suggested by our method often include the gold standard in second or third place. Remarkably, the method also performs quite well on the subject-verb agreement error type. Of the suggestions provided by our method the gold standard correction comes in third place.