摘要:抽象语义表示（abstract meaning representation，简称AMR）文本生成的任务是给定AMR图，生成与其语义一致的文本.相关工作表明，人工标注语料的规模大小直接影响了AMR文本生成的性能.为了降低对人工标注语料的依赖，提出了基于多任务预训练的AMR文本生成方法.特别地，基于大规模自动标注AMR语料，提出与AMR文本生成任务相关的3个预训练任务，分别是AMR降噪自编码、句子降噪自编码以及AMR文本生成任务本身.此外，基于预训练模型，在朴素微调方法的基础上，进一步提出了基于多任务训练的微调方法，使得最终模型不仅适用于AMR文本生成，同时还适用于预训练任务.基于两个AMR标准数据集的实验结果表明：使用0.39M自动标注数据，提出的预训练方法能够大幅度提高AMR文本生成的性能，在AMR2.0和AMR3.0上分别提高了12.27和7.57个BLEU值，性能分别达到40.30和38.97.其中，在AMR2.0上的性能为目前报告的最优值，在AMR3.0上的性能为目前为止首次报告的性能.



Abstract:Given an AMR (abstract meaning representation) graph, AMR-to-text generation aims to generate text with the same meaning. Related studies show that the performance of AMR-to-text severely suffers from the size of the manually annotated dataset. To alleviate the dependence on manually annotated dataset, this study proposes a novel multi-task pre-training for AMR-to-text generation. In particular, based on a large-scale automatic AMR dataset, three relevant pre-training tasks are defined, i.e., AMR denoising auto-encoder, sentence denoising auto-encoder, and AMR-to-text generation itself. In addition, to fine-tune the pre-training models, the vanilla fine-tuning method is further extended to multi-task learning fine-tuning, which enables the final model to maintain performance on both AMR-to-text and pre-training tasks. With the automatic dataset of 0.39M sentences, detailed experimentation on two AMR benchmarks shows that the proposed pre-training approach significantly improves the performance of AMR-to-text generation, with the improvement of 12.27 BLEU on AMR2.0 and 7.57 on AMR3.0, respectively. This greatly advances the state-of-the-art performance with 40.30 BLEU on AMR2.0 and 38.97 on AMR 3.0, respectively. To the best knowledge, this is the best result achieved so far on AMR 2.0 while AMR-to-text generation performance on AMR 3.0 is firstly reported.



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