【摘要】 抽象语义表示AMR是将给定文本的语义特征抽象成一个单根的有向无环图。由于缺乏非英文语言的AMR数据集，跨语言AMR解析通常指给定非英文目标语言文本，构建其英文翻译对应的AMR图。目前跨语言AMR解析的相关工作均基于大规模英文-目标语言平行语料或高性能英文-目标语言翻译模型，通过构建(英文，目标语言和AMR)三元平行语料进行目标语言的AMR解析。与该假设不同的是，本文探索在仅具备大规模单语英文和单语目标语言语料的情况下，实现跨语言AMR解析。为此，提出基于无监督预训练的跨语言AMR解析方法。具体地，在预训练过程中，融合无监督神经机器翻译任务、英文和目标语言AMR解析任务；在微调过程中，使用基于英文AMR 2.0转换的目标语言AMR数据集进行单任务微调。基于AMR 2.0和多语言AMR测试集的实验结果表明，所提方法在德文、西班牙文和意大利文上分别获得了67.89%, 68.04%和67.99%的Smatch F1值。更多还原

【Abstract】 AMR(Abstract Meaning Representation) abstracts the semantic features of a given text into a single-root directed acyclic graph. Due to the lack of non-English language AMR datasets, cross-lingual AMR parsing aims to parse non-English text into the corresponding AMR graph of its English translation. Current cross-lingual AMR parsing methods rely on large-scale English-target language parallel corpora or high-performance English-target language translation models to build(English, target language, AMR) triplet parallel corpora for target language AMR parsing. In contrast to this assumption, this paper explores the possibility of achieving cross-lingual AMR parsing with only large-scale monolingual English and target language corpora. To this end, we propose cross-lingual AMR parsing based on unsupervised pretraining. Specifically, during pretraining, we integrate unsupervised neural machine translation tasks, English AMR parsing tasks, and target language AMR parsing tasks. During fine-tuning, we use an English AMR2.0-based target language AMR dataset for single-task fine-tuning. Experimental results on AMR2.0 and a multilingual AMR test set show that our method achieves Smatch F1 scores of 67.89, 68.04, and 67.99 in German, Spanish, and Italian, respectively.更多还原