Neural Cross-Lingual Relation Extraction Based on Bilingual Word Embedding Mapping
Table 4: Comparison of the performance (F1 score) using different mappings on the target-language development data under the Bi-LSTM model.
['[BOLD] Mapping', '[BOLD] German', '[BOLD] Spanish', '[BOLD] Italian', '[BOLD] Japanese', '[BOLD] Portuguese', '[BOLD] Arabic', '[BOLD] Chinese', '[BOLD] Average']
The regular mapping outperforms the orthogonal mapping consistently across the target languages. While the orthogonal mapping was shown to work better than the regular mapping for the word translation task Xing et al. ; Smith et al. , our cross-lingual RE approach directly maps target-language word embeddings to the English embedding space without conducting word translations. Moreover, the orthogonal mapping requires length normalization, but we observed that length normalization adversely affects the performance of the English RE models (about 2.0 F1 points drop).