Automatic Generation of High Quality CCGbanks for Parser Domain Adaptation
Table 1: The performance of baseline CCG parsers and the proposed converter on WSJ23, where UF1 and LF1 represents unlabeled and labeled F1, respectively.
['Method', 'UF1', 'LF1']
In short, the method of depccg is equivalent to omitting the dependence on a dependency tree z from P(y|x,z) of our converter model, and running an A* parsing-based decoder on ptag|dep calculated on h1,... ,hN =Ω(ex1,... ,exN), as in our method. In this work, on top of that, we include as a baseline a setting where the affix vectors are replaced by contextualized word representation (ELMo; Peters et al. This can be regarded as evaluating the upper bound of the conversion quality, since the evaluated data comes from the same domain as the converter’s training data. Our converter shows much higher scores compared to the current best-performing depccg combined with ELMo (1.5% and 2.17% up in unlabeled/labeled F1 scores), suggesting that, using the proposed converter, we can obtain CCGbanks of high quality.