This paper presents a novel semantic representation, WISeR, that overcomes challenges for Abstract Meaning Representation (AMR). Despite its strengths, AMR is not easily applied to languages or domains without predefined semantic frames, and its use of numbered arguments results in semantic role labels, which are not directly interpretable and are semantically overloaded for parsers. We examine the numbered arguments of predicates in AMR and convert them to thematic roles that do not require reference to semantic frames. We create a new corpus of 1K English dialogue sentences annotated in both WISeR and AMR. WISeR shows stronger inter-annotator agreement for beginner and experienced annotators, with beginners becoming proficient in WISeR annotation more quickly. Finally, we train a state-of-the-art parser on the AMR 3.0 corpus and a WISeR corpus converted from AMR 3.0. The parser is evaluated on these corpora and our dialogue corpus. The WISeR model exhibits higher accuracy than its AMR counterpart across the board, demonstrating that WISeR is easier for parsers to learn.

翻译：本文提出了一种新型语义表示WISeR，克服了抽象语义表示面临的挑战。尽管AMR具有诸多优势，但其难以应用于缺乏预定义语义框架的语言或领域，且其带编号论元的使用导致了语义角色标签，这些标签既不可直接解释，又给解析器带来语义过载问题。我们考察了AMR中谓词的带编号论元，并将其转换为无需依赖语义框架的主题角色。我们构建了一个包含1000个英语对话句子的新语料库，这些句子同时以WISeR和AMR两种表示进行标注。WISeR在初学者和经验丰富的标注者之间展现出更强的一致性，且初学者能更快掌握WISeR的标注方法。最后，我们在AMR 3.0语料库和由其转换而成的WISeR语料库上训练了当前最先进的解析器，并在这些语料库及我们的对话语料库上对解析器进行了评估。WISeR模型在所有评估指标上均表现出比AMR对应模型更高的准确率，表明WISeR更容易被解析器学习。