An Edit-centric Approach for Wikipedia Article Quality Assessment
Table 1: Impact of the parameters on validation performance for the WWII article history.
['[BOLD] Model', '[BOLD] F1', '[BOLD] Acc', '[BOLD] BLEU']
These results also shed some light on the trade-off between tasks for different values of λ. We see that although a higher value tends to give better classification performance both in terms of F1-Score and accuracy, it is also possible to see that there is a sweet-spot that allows the classification to benefit from learning an edit-message mapping, supporting our hypothesis. Moreover, this comes at a negligible variation in terms of BLEU scores, as seen when we compare against a pure message-generation task (Only Generation on the table).