A Discriminative Neural Model for Cross-Lingual Word Alignment
Table 4: F1 results on OntoNotes test for systems trained on data projected via FastAlign and DiscAlign.
['Method', '# train', 'P', 'R', 'F1']
Based on our findings that: [topsep=0.0pt,itemsep=-1ex,partopsep=1ex,parsep=1ex] discriminative alignments outperform common unsupervised baselines in two typologically divergent languages this performance boost leads to major downstream improvements on NER only a small amount of labelled data is needed to realize these improvements and that these labelled examples can be obtained from L2 speakers with minimal training we conclude with a call for additional annotation efforts in a wider variety of languages. While multilingual datasets directly annotated for a given task typically lead to the highest-performing systems (see these datasets are task-specific and not robust to ontology changes. In contrast, the framework of projection via discriminatively-trained alignments which we present is task-agnostic (any token-level annotations can be projected) and requires only one source-side dataset to be annotated.