论文标题
在线向AMR到文本生成的反向放置
Online Back-Parsing for AMR-to-Text Generation
论文作者
论文摘要
AMR到文本生成旨在恢复包含与输入AMR图相同含义的文本。当前的研究开发了越来越强大的图形编码器，以更好地表示AMR图，并基于用于生成输出的标准语言建模的解码器。我们提出了一个解码器，该解码器可预测文本生成期间目标句子的投影AMR图。结果，我们的输出可以更好地保留输入含义，而不是标准解码器。两个AMR基准的实验表明，基于Graph Transformer的先前最新系统，我们的模型优越性。
AMR-to-text generation aims to recover a text containing the same meaning as an input AMR graph. Current research develops increasingly powerful graph encoders to better represent AMR graphs, with decoders based on standard language modeling being used to generate outputs. We propose a decoder that back predicts projected AMR graphs on the target sentence during text generation. As the result, our outputs can better preserve the input meaning than standard decoders. Experiments on two AMR benchmarks show the superiority of our model over the previous state-of-the-art system based on graph Transformer.
