Current open-domain neural semantics parsers show impressive performance. However, closer inspection of the symbolic meaning representations they produce reveals significant weaknesses: sometimes they tend to merely copy character sequences from the source text to form symbolic concepts, defaulting to the most frequent word sense based in the training distribution. By leveraging the hierarchical structure of a lexical ontology, we introduce a novel compositional symbolic representation for concepts based on their position in the taxonomical hierarchy. This representation provides richer semantic information and enhances interpretability. We introduce a neural "taxonomical" semantic parser to utilize this new representation system of predicates, and compare it with a standard neural semantic parser trained on the traditional meaning representation format, employing a novel challenge set and evaluation metric for evaluation. Our experimental findings demonstrate that the taxonomical model, trained on much richer and complex meaning representations, is slightly subordinate in performance to the traditional model using the standard metrics for evaluation, but outperforms it when dealing with out-of-vocabulary concepts. This finding is encouraging for research in computational semantics that aims to combine data-driven distributional meanings with knowledge-based symbolic representations.

翻译：当前开放领域的神经语义解析器表现出令人印象深刻的性能。然而，对其产生的符号意义表示进行仔细检查，揭示了显著缺陷：有时它们倾向于仅从源文本中复制字符序列以形成符号概念，并默认基于训练分布中最频繁的词义。通过利用词汇本体的层次结构，我们引入了一种基于概念在分类层级中位置的新型组合符号表示。该表示提供了更丰富的语义信息并增强了可解释性。我们引入了一种神经“分类”语义解析器，以利用这种新的谓词表示系统，并将其与在传统意义表示格式上训练的标准神经语义解析器进行比较，采用新颖的挑战集和评估指标进行评价。实验结果表明，尽管在标准指标下，基于更丰富、更复杂意义表示训练的分类模型在性能上略逊于传统模型，但在处理词汇外概念时，其表现优于传统模型。这一发现对于旨在将数据驱动的分布意义与基于知识的符号表示相结合的计算语义学研究具有积极启示。