A Nested Attention Neural Hybrid Model for Grammatical Error Correction
Table 5: F0.5 results on the CoNLL-13 set of main model architectures, on different segments of the set according to whether the input contains OOVs.
['[BOLD] Model', '[BOLD] NonOOV', '[BOLD] OOV', '[BOLD] Overall']
We present a comparative performance analysis of models on the CoNLL-13 development set. First, we divide the set into two segments: OOV and NonOOV, based on whether there is at least one OOV word in the given source input. The additional nested character-level attention of our hybrid model brings a sizable improvement over the basic hybrid model in the OOV segment and a small degradation in the non-OOV segment. We should note that in future work character-level attention can be added for non-OOV source words in the nested attention model, which could improve performance on this segment as well.