Abstract
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.
Abstract (translated)
目前公开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的近距离观察揭示了显著的弱点：有时候它们倾向于仅仅从源文本中复制字符序列以形成符号概念，默认为基于训练分布中最常见单词意义的最频词汇。通过利用词汇本体的层次结构，我们引入了一种基于它们在分类层次结构中的位置的新组合符号表示概念。这种表示提供了更丰富的语义信息并提高了可解释性。我们引入了一个神经“语义分类”语义解析器，用于利用这种基于命题的新表示系统，并将其与使用传统意义表示格式训练的标准神经语义解析器进行比较。我们的实验结果表明，基于更丰富和复杂语义表示的语义模型在标准评估指标上的性能略微低于使用标准评估指标的传统模型，但在处理非词汇概念时表现优异。这一发现对于旨在将数据驱动的分布语义与知识驱动的符号表示相结合的计算语义研究来说是有益的。
URL
https://arxiv.org/abs/2404.12698
