A Deep Neural Network for Chinese Zero Pronoun Resolution
Table 3: Ablation experimental results on the whole test data.
['[EMPTY]', 'R', 'P', 'F']
In each ablation experiment, we retrain the model with only one type of representations of candidates utilized as inputs. To minimize the external influence, we simply employ experimental Setting 1 (gold parse and gold AZP) to perform the experiments. We can observe that incorporating the global information significantly boosts the F-score comparing with the model utilize only the local information. Meanwhile, local representations of candidate antecedents provide the system with a strong ability to reveal the semantics of the phrases. Consequently, when the local information is ablated (i.e., without utilizing l(npi) when computing the formula 2), F-score drops by 0.7% comparing with the full system.