跨语言回解析：面向零资源语义解析的从意义表示生成话语
Cross-lingual Back-Parsing: Utterance Synthesis from Meaning Representation for Zero-Resource Semantic Parsing
AI总结：
本文提出跨语言回解析（CBP）方法，利用多语言预训练语言模型的表示几何结构，从源语言意义表示合成目标语言话语，在零资源条件下有效提升语义解析的跨语言迁移性能。
摘要：
近期研究致力于利用多语言预训练语言模型（mPLMs）在无需大量标注的情况下将语义解析（SP）扩展到多种语言。然而，实现语义解析的零样本跨语言迁移仍然具有挑战性，导致源语言与目标语言之间存在性能差距。在本研究中，我们提出跨语言回解析（CBP），这是一种新颖的数据增强方法，旨在增强语义解析的跨语言迁移。CBP利用mPLMs的表示几何结构，从源语言的意义表示合成目标语言话语。我们的方法仅利用源语言的标注数据和单语语料库，即可在具有挑战性的零资源设置下有效执行跨语言数据增强。在两个跨语言语义解析基准（Mschema2QA和Xspider）上的大量实验表明，CBP在目标语言上带来了显著提升。对合成话语的进一步分析表明，我们的方法成功生成了具有高槽值对齐率的目标语言话语，同时保持了语义完整性。我们的代码和数据已在 https://github.com/deokhk/CBP 公开。
英文摘要：
Recent efforts have aimed to utilize multilingual pretrained language models (mPLMs) to extend semantic parsing (SP) across multiple languages without requiring extensive annotations. However, achieving zero-shot cross-lingual transfer for SP remains challenging, leading to a performance gap between source and target languages. In this study, we propose Cross-Lingual Back-Parsing (CBP), a novel data augmentation methodology designed to enhance cross-lingual transfer for SP. Leveraging the representation geometry of the mPLMs, CBP synthesizes target language utterances from source meaning representations. Our methodology effectively performs cross-lingual data augmentation in challenging zero-resource settings, by utilizing only labeled data in the source language and monolingual corpora. Extensive experiments on two cross-language SP benchmarks (Mschema2QA and Xspider) demonstrate that CBP brings substantial gains in the target language. Further analysis of the synthesized utterances shows that our method successfully generates target language utterances with high slot value alignment rates while preserving semantic integrity. Our codes and data are publicly available at https://github.com/deokhk/CBP.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
