Previous CCG supertaggers usually predict categories using multi-class
classification. Despite their simplicity, internal structures of categories are
usually ignored. The rich semantics inside these structures may help us to
better handle relations among categories and bring more robustness into
existing supertaggers. In this work, we propose to generate categories rather
than classify them: each category is decomposed into a sequence of smaller
atomic tags, and the tagger aims to generate the correct sequence. We show that
with this finer view on categories, annotations of different categories could
be shared and interactions with sentence contexts could be enhanced. The
proposed category generator is able to achieve state-of-the-art tagging (95.5%
accuracy) and parsing (89.8% labeled F1) performances on the standard CCGBank.
Furthermore, its performances on infrequent (even unseen) categories,
out-of-domain texts and low resource language give promising results on
introducing generation models to the general CCG analyses.

通过生成类别而不是分类类别，我们提出了一种新的 CCG supertagger 方法，将每个类别分解为一系列更小的标记，以此更好地处理类别间的关系并增强与句子上下文的交互。该方法能够在标准 CCGBank 上实现最先进的标识 (95.5% 精确度) 和分析 (89.8% 标记 F1)，并在罕见类别、领域外文本和资源匮乏语言方面取得了有希望的结果。