What do Entity-Centric Models Learn? Insights from Entity Linking in Multi-Party Dialogue
Table 1: Model parameters and results on the character identification task. First block: top systems at SemEval 2018. Results in the second block marked with ∗ are statistically significantly better than biLSTM at p<0.001 (approximate randomization tests, Noreen, 1989).
['models', '#par', 'all (78) F1', 'all (78) Acc', 'main (7) F1', 'main (7) Acc']
Aina et al. ; Park et al. Recall that our goal in this paper is not to optimize performance, but to understand model behavior; however, results show that these models are worth analyzing, as that they outperform the state of the art. All models perform on a par on main entities, but entity-centric models outperform biLSTM by a substantial margin when all characters are to be predicted (the difference between EntLib and EntNet is not significant).