A Discriminative Neural Model for Cross-Lingual Word Alignment
Table 2: Precision, Recall, and F1 on Chinese GALE test data. BPE indicates “with BPE”, and NER denotes restriction to NER spans.
['Method', 'P', 'R', 'F1']
Our model outperforms both baselines for all languages and experimental conditions. By increasing the threshold α above which p(ai,j|s,t) is considered an alignment we can obtain high-precision alignments, exceeding FastAlign’s precision and recall. The best threshold values on the development set were α=0.13 and α=0.14 for average attention on data with and without BPE (respectively) and α=0.15 for the discriminative settings (α=0.5 was used for the high-precision “+prec” setting).