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.

本文提出了一种新的语义表示方法WISeR，突破了抽象意义表示(AMR)的挑战。我们将AMR谓词的编号参数转换为不需要引用语义框架的主题角色，创建了一个新的包含1K个英语对话句子的WISeR和AMR注释语料库，并训练了一个具有最先进的AMR 3.0和WISeR语料库的解析器。WISeR模型在各方面都比AMR模型表现出更高的准确性，证明解析器更容易学习WISeR方法。

广泛可解释的语义表示：用于更广泛适用性的无框架意义表示