Neural Cross-Lingual Coreference Resolution And Its Application To Entity Linking
Table 3: Coreference results on the Es and Zh test sets of TAC 15. En model performs competitively to the models trained on target language data.
['[EMPTY]', 'MUC', 'B3', 'CEAF', 'CoNLL']
“Es Model” refers to the model trained on Es training set of TAC 15 using Es embeddings. “Zh Model” refers to the model trained on the Zh training set of TAC 15 using Zh embeddings. The En model performs 0.5 point below the Es model on the Es test set. On the Zh test set, the En model performs only 0.3 point below the Zh model. Hence, we show that without using any target language training data, the En model with multi-lingual embeddings gives comparable results to models trained on the target language.