Parsing sentences to linguistically-expressive semantic representations is a
key goal of Natural Language Processing. Yet statistical parsing has focused
almost exclusively on bilexical dependencies or domain-specific logical forms.
We propose a neural encoder-decoder transition-based parser which is the first
full-coverage semantic graph parser for Minimal Recursion Semantics (MRS). The
model architecture uses stack-based embedding features, predicting graphs
jointly with unlexicalized predicates and their token alignments. Our parser is
more accurate than attention-based baselines on MRS, and on an additional
Abstract Meaning Representation (AMR) benchmark, and GPU batch processing makes
it an order of magnitude faster than a high-precision grammar-based parser.
Further, the 86.69% Smatch score of our MRS parser is higher than the
upper-bound on AMR parsing, making MRS an attractive choice as a semantic
representation.

该研究提出了一个基于神经编码器 - 解码器的转移句法分析器，可以作为关于最小递归语义的完全覆盖的语义图分析器，预测图与非词汇化谓词及其标记对齐共同。该分析器比注意力模型基准更准确，并且在 GPU 批处理方面比高精度基于语法的分析器快一个数量级。此外，我们的最小递归语义分析器的 86.69％ Smatch 得分高于 AMR 分析的上限，这使得 MRS 成为有吸引力的语义表示选择。