Improving coreference resolutionwith automatically predicted prosodic information
Table 1: Pitch accent presence
['Baseline', '46.11', '[EMPTY]']
As expected, the numbers are higher if we limit this feature to short NPs. We believe that this is due to the fact that the feature contributes most when it is most meaningful: on short NPs, a pitch accent makes it more likely for the NP to contain new information, whereas long NPs almost always have at least one pitch accent, regardless of its information status. We achieve the highest performance with gold labels, followed by the gold/auto version with a score that is not significantly worse than the gold version. This is important for applications as it suggests that the loss in performance is small when training on gold data and testing on predicted data. As expected, the version that is trained and tested on predicted data performs worse, but is still significantly better than the baseline. Hence, prosodic information is helpful in all three settings. It also shows that the assumption on short NPs in the pilot study is also true for automatic labels.