The Parallel Meaning Bank (PMB) serves as a corpus for semantic processing with a focus on semantic parsing and text generation. Currently, we witness an excellent performance of neural parsers and generators on the PMB. This might suggest that such semantic processing tasks have by and large been solved. We argue that this is not the case and that performance scores from the past on the PMB are inflated by non-optimal data splits and test sets that are too easy. In response, we introduce several changes. First, instead of the prior random split, we propose a more systematic splitting approach to improve the reliability of the standard test data. Second, except for the standard test set, we also propose two challenge sets: one with longer texts including discourse structure, and one that addresses compositional generalization. We evaluate five neural models for semantic parsing and meaning-to-text generation. Our results show that model performance declines (in some cases dramatically) on the challenge sets, revealing the limitations of neural models when confronting such challenges.

翻译：并行意义库（PMB）是一个专注于语义解析和文本生成的语义处理语料库。目前，神经解析器和生成器在PMB上表现出色。这可能表明此类语义处理任务已基本得到解决。我们认为情况并非如此，过去在PMB上的性能得分因非最优的数据划分和过于简单的测试集而被高估。为此，我们引入了若干改进。首先，相较于先前的随机划分，我们提出了一种更系统的划分方法以提高标准测试数据的可靠性。其次，除标准测试集外，我们还提出了两个挑战集：一个包含含篇章结构的长文本，另一个针对组合泛化能力。我们评估了五种用于语义解析和意义到文本生成的神经模型。结果表明，模型性能在挑战集上出现下降（某些情况下极为显著），揭示了神经模型在面对此类挑战时的局限性。