Sequence Labeling Parsing by Learning Across Representations
Table 2: Results on the ptb and spmrl test sets.
['[EMPTY]', 'Model', 'Dependency Parsing UAS', 'Dependency Parsing LAS', 'Constituency Parsing', 'Constituency Parsing F1']
On average, mtl models with auxiliary losses achieve the best performance for both parsing abstractions. They gain 1.14 F1 points on average in comparison with the single model for constituency parsing, and 0.62 uas and 0.15 las points for dependency parsing. In comparison to the single-paradigm MTL models, the average gain is smaller: 0.05 f1 points for constituency parsing, and 0.09 uas and 0.21 las points for dependency parsing.