Neural Cross-Lingual Relation Extraction Based on Bilingual Word Embedding Mapping
Table 3: Performance of the supervised English RE models on the in-house and ACE05 English test data.
['[BOLD] Model', '[BOLD] In-House  [BOLD] P', '[BOLD] In-House  [BOLD] R', '[BOLD] In-House  [BOLD] F1', '[BOLD] ACE05  [BOLD] P', '[BOLD] ACE05  [BOLD] R', '[BOLD] ACE05  [BOLD] F1']
Note that adding an additional context layer with either Bi-LSTM or CNN significantly improves the performance of our English RE model, compared with the simple Pass-Through model. Therefore, we will focus on the Bi-LSTM model and the CNN model in the subsequent experiments.