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Sequence Labeling Parsing by Learning across Representations

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Figure

Figure 1: An example of constituency and dependencytrees with their encodings.
Table 1: Comparison of the single-paradigm modelsfor dependency parsing evaluated on the PTB dev setwhere each label is learned as single, 2- or 3-tasks.
Table 2parsing.MTL models, the average gain is smaller: 0.05points for constituency parsing, and 0.090.21son with the single model for constituency parsing,and compares single-paradigm models againsttheir double-paradigm MTL versions.On aver-age, MTL models with auxiliary losses achievethe best performance for both parsing abstractions.They gain 1.05 F1 points on average in compari- 0.62 UAS and 0.15 LAS points for dependencyIn comparison to the single-paradigm F1 UAS and LAS points for dependency parsing.
Table 4: Dependency parsing: existing models evaluated with LAS scores on the SPMRL test set.
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