Universal Word Segmentation: Implementation and Interpretation
Table 12: Extrinsic evaluations with dependency parsing on the test sets. The parsing accuracies are reported in unlabelled attachment score (UAS) and labelled attachment score (LAS).
['[EMPTY]', 'Segmentation Accuracy', 'Segmentation Accuracy', 'UDPipe parser UAS', 'UDPipe parser UAS', 'UDPipe parser LAS', 'UDPipe parser LAS', 'dozat-qi-manning:2017:K17-3 UAS', 'dozat-qi-manning:2017:K17-3 UAS', 'dozat-qi-manning:2017:K17-3 LAS', 'dozat-qi-manning:2017:K17-3 LAS']
As a form of extrinsic evaluation, we test the segmenter in a dependency parsing setup on the datasets where we obtained substantial improvements over UDPipe. We present results for the transition-based parsing model in UDPipe 1.2 and for the graph-based parser by \newcitedozat-qi-manning:2017: K17-3. We can see that word segmentation accuracy has a great impact on parsing accuracy as the segmentation errors propagate. Having a more accurate word segmentation model is very beneficial for achieving higher parsing accuracy.