Broad-coverage meaning representations in NLP mostly focus on explicitly expressed content. More importantly, the scarcity of datasets annotating diverse implicit roles limits empirical studies into their linguistic nuances. For example, in the web review "Great service!", the provider and consumer are implicit arguments of different types. We examine an annotated corpus of fine-grained implicit arguments (Cui and Hershcovich, 2020) by carefully re-annotating it, resolving several inconsistencies. Subsequently, we present the first transition-based neural parser that can handle implicit arguments dynamically, and experiment with two different transition systems on the improved dataset. We find that certain types of implicit arguments are more difficult to parse than others and that the simpler system is more accurate in recovering implicit arguments, despite having a lower overall parsing score, attesting current reasoning limitations of NLP models. This work will facilitate a better understanding of implicit and underspecified language, by incorporating it holistically into meaning representations.

翻译：更为重要的是,由于数据集缺乏说明各种隐含作用的数据集,限制了对其语言细微差别的经验研究。例如,在“卓越服务”的网页审查中,提供者和消费者是不同类型的隐含论点。我们仔细重新说明隐含的细微论点(Cui和Hershcovich, 2020年),通过仔细重新说明这些论点,解决若干不一致之处,来审查一系列附带注释的细微隐含论点(Cui和Hershcovich, 2020年)。随后,我们提出了第一个能够动态处理隐含论点的过渡性神经剖析器,并在改进的数据集上试验了两种不同的过渡系统。我们发现,某些类型的隐含论点比其他论点更难分析,而较简单的系统在收回隐含论点方面更准确,尽管总体分数较低,但测试了NLP模型目前的推理局限性。这项工作将促进更好地理解隐含和未加说明的语言,将它整体地纳入含义表述中。