Solving Hard Coreference Problems
Table 7: Performance results on Winograd and WinoCoref datasets. All our three systems are trained on WinoCoref, and we evaluate the predictions on both datasets. Our systems improve over the baselines by over than 20% on Winograd and over 15% on WinoCoref.
['Dataset', 'Metric', 'Illinois', 'IlliCons', 'rahman2012resolving', 'KnowFeat', 'KnowCons', 'KnowComb']
Performance results on Winograd and WinoCoref datasets are shown in Table 7. The best performing system is KnowComb. It improves by over 20% over a state-of-art general coreference system on Winograd and also outperforms Rahman and Ng (2012) by a margin of 3.3%. On the WinoCoref dataset, it improves by 15%. These results show significant performance improvement by using Predicate Schemas knowledge on hard coreference problems. Note that the system developed in Rahman and Ng (2012) cannot be used on the WinoCoref dataset. The results also show that it is better to compile knowledge into constraints when the knowledge quality is high than add them as features.