Discourse Representation Structure (DRS) is an innovative semantic
representation designed to capture the meaning of texts with arbitrary lengths
across languages. The semantic representation parsing is essential for
achieving natural language understanding through logical forms. Nevertheless,
the performance of DRS parsing models remains constrained when trained
exclusively on monolingual data. To tackle this issue, we introduce a
cross-lingual training strategy. The proposed method is model-agnostic yet
highly effective. It leverages cross-lingual training data and fully exploits
the alignments between languages encoded in pre-trained language models. The
experiments conducted on the standard benchmarks demonstrate that models
trained using the cross-lingual training method exhibit significant
improvements in DRS clause and graph parsing in English, German, Italian and
Dutch. Comparing our final models to previous works, we achieve
state-of-the-art results in the standard benchmarks. Furthermore, the detailed
analysis provides deep insights into the performance of the parsers, offering
inspiration for future research in DRS parsing. We keep updating new results on
benchmarks to the appendix.

通过引入跨语言训练策略，深入分析和改进基于 Discourse Representation Structure（DRS）的语义表示分析模型，在英语、德语、意大利语和荷兰语的标准基准测试中取得了最先进的结果，并为未来的 DRS 分析研究提供了深入的见解。