A Discriminative Neural Model for Cross-Lingual Word Alignment
Table 7: Sentences per minute and average scores against gold-labelled data for sentences annotated for alignment by human annotators (Hu.), compared to DiscAlign (DA) on the same sentences.
['Model', 'sents/m', 'P', 'R', 'F1']
The annotators achieve high overall precision for alignment, but fall short on recall. When only alignments of NER spans are considered, their F1 score improves considerably. Additionally, human annotators outperform DiscAlign when evaluated on the same sentences.