Thanks to the strong representation power of neural encoders, neural
chart-based parsers have achieved highly competitive performance by using local
features. Recently, it has been shown that non-local features in CRF structures
lead to improvements. In this paper, we investigate injecting non-local
features into the training process of a local span-based parser, by predicting
constituent n-gram non-local patterns and ensuring consistency between
non-local patterns and local constituents. Results show that our simple method
gives better results than the self-attentive parser on both PTB and CTB.
Besides, our method achieves state-of-the-art BERT-based performance on PTB
(95.92 F1) and strong performance on CTB (92.31 F1). Our parser also achieves
better or competitive performance in multilingual and zero-shot cross-domain
settings compared with the baseline.

本文研究将非局部特征注入本地基于跨度的解析器的训练过程中，通过预测组成成分 n-gram 非局部模式并确保非局部模式与本地成分的一致性，结果显示，我们的方法在 PTB 和 CTB 上均优于自我注意解析器，在 PTB 上达到 BERT 的最新性能（95.92 F1），并在 CTB 上获得强大的性能（92.31 F1）。与基线相比，在多语言和零 - shot 跨领域设置中，我们的解析器也实现了更好或具有竞争力的性能。

探究神经组成句法分析的非局部特征

Investigating Non-local Features for Neural Constituency Parsing

Despite the success of sequence-to-sequence (seq2seq) models in semantic
parsing, recent work has shown that they fail in compositional generalization,
i.e., the ability to generalize to new structures built of components observed
during training. In this work, we posit that a span-based parser should lead to
better compositional generalization. we propose SpanBasedSP, a parser that
predicts a span tree over an input utterance, explicitly encoding how partial
programs compose over spans in the input. SpanBasedSP extends Pasupat et al.
(2019) to be comparable to seq2seq models by (i) training from programs,
without access to gold trees, treating trees as latent variables, (ii) parsing
a class of non-projective trees through an extension to standard CKY. On
GeoQuery, SCAN and CLOSURE datasets, SpanBasedSP performs similarly to strong
seq2seq baselines on random splits, but dramatically improves performance
compared to baselines on splits that require compositional generalization: from
$61.0 \rightarrow 88.9$ average accuracy.

本研究提出了 SpanBasedSP，这是一种基于跨度的解析器，用于语义分析，并且能够更好地进行组合概括。SpanBasedSP 在几个数据集上进行测试，表现出与强 seq2seq 基线相当的效果，并且在需要进行组合概括的情况下，与基线相比有显著的性能优势。