Question: How is dependency parsing empirically verified?
Evidence: We evaluate our model on two benchmark treebanks, English Penn Treebank (PTB) and Chinese Penn Treebank (CTB5.1) following standard data splitting BIBREF30, BIBREF31. POS tags are predicted by the Stanford Tagger BIBREF32. For constituent parsing, we use the standard evalb tool to evaluate the F1 score. For dependency parsing, we apply Stanford basic dependencies (SD) representation BIBREF4 converted by the Stanford parser. Following previous work BIBREF27, BIBREF33, we report the results without punctuations for both treebanks.
Tables TABREF17, TABREF18 and TABREF19 compare our model to existing state-of-the-art, in which indicator Separate with our model shows the results of our model learning constituent or dependency parsing separately, (Sum) and (Concat) respectively represent the results with the indicated input token representation setting. On PTB, our model achieves 93.90 F1 score of constituent parsing and 95.91 UAS and 93.86 LAS of dependency parsing. On CTB, our model achieves a new state-of-the-art result on both constituent and dependency parsing. The comparison again suggests that learning jointly in our model is superior to learning separately. In addition, we also augment our model with ELMo BIBREF48 or a larger version of BERT BIBREF49 as the sole token representation to compare with other pre-training models. Since BERT is based on sub-word, we only take the last sub-word vector of the word in the last layer of BERT as our sole token representation $x_i$. Moreover, our single model of BERT achieves competitive performance with other ensemble models.
Multitask learning (MTL) is a natural solution in neural models for multiple inputs and multiple outputs, which is adopted in this work to decode constituent and dependency in a single model. BIBREF15 indicates that when tasks are sufficiently similar, especially with syntactic nature, MTL would be useful. In contrast to previous work on deep MTL BIBREF16, BIBREF17, our model focuses on more related tasks and benefits from the strong inherent relation. At last, our model is evaluated on two benchmark treebanks for both constituent and dependency parsing. The empirical results show that our parser reaches new state-of-the-art for all parsing tasks.
We evaluate our model on two benchmark treebanks, English Penn Treebank (PTB) and Chinese Penn Treebank (CTB5.1) following standard data splitting BIBREF30, BIBREF31. POS tags are predicted by the Stanford Tagger BIBREF32. For constituent parsing, we use the standard evalb tool to evaluate the F1 score. For dependency parsing, we apply Stanford basic dependencies (SD) representation BIBREF4 converted by the Stanford parser. Following previous work BIBREF27, BIBREF33, we report the results without punctuations for both treebanks.
Tables TABREF17, TABREF18 and TABREF19 compare our model to existing state-of-the-art, in which indicator Separate with our model shows the results of our model learning constituent or dependency parsing separately, (Sum) and (Concat) respectively represent the results with the indicated input token representation setting. On PTB, our model achieves 93.90 F1 score of constituent parsing and 95.91 UAS and 93.86 LAS of dependency parsing. On CTB, our model achieves a new state-of-the-art result on both constituent and dependency parsing.
At last, our model is evaluated on two benchmark treebanks for both constituent and dependency parsing. The empirical results show that our parser reaches new state-of-the-art for all parsing tasks.
Answer: The answers are shown as follows:
*  At last, our model is evaluated on two benchmark treebanks for both constituent and dependency parsing. The empirical results show that our parser reaches new state-of-the-art for all parsing tasks.