English news headlines form a register with unique syntactic properties that
have been documented in linguistics literature since the 1930s. However,
headlines have received surprisingly little attention from the NLP syntactic
parsing community. We aim to bridge this gap by providing the first news
headline corpus of Universal Dependencies annotated syntactic dependency trees,
which enables us to evaluate existing state-of-the-art dependency parsers on
news headlines. To improve English news headline parsing accuracies, we develop
a projection method to bootstrap silver training data from unlabeled news
headline-article lead sentence pairs. Models trained on silver headline parses
demonstrate significant improvements in performance over models trained solely
on gold-annotated long-form texts. Ultimately, we find that, although projected
silver training data improves parser performance across different news outlets,
the improvement is moderated by constructions idiosyncratic to outlet.

通过提供第一个标记有语法依存树的通用语言标题新闻语料库，我们旨在弥补语言处理句法分析领域对新闻标题的关注不足。通过从未标记的新闻标题 - 文章引文对中获取银标签训练数据来改进英语新闻标题的分析准确性，我们发现训练基于银标题解析的模型比仅基于黄金注释长格式文本训练的模型性能有显着提高。最终，我们发现尽管投影的银标签训练数据可以提高解析器在不同新闻媒体上的性能，但是该提高是受特定出版物特有结构的调节的。

弱监督标题依赖关系分析

Weakly Supervised Headline Dependency Parsing

Neural methods have had several recent successes in semantic parsing, though
they have yet to face the challenge of producing meaning representations based
on formal semantics. We present a sequence-to-sequence neural semantic parser
that is able to produce Discourse Representation Structures (DRSs) for English
sentences with high accuracy, outperforming traditional DRS parsers. To
facilitate the learning of the output, we represent DRSs as a sequence of flat
clauses and introduce a method to verify that produced DRSs are well-formed and
interpretable. We compare models using characters and words as input and see
(somewhat surprisingly) that the former performs better than the latter. We
show that eliminating variable names from the output using De Bruijn-indices
increases parser performance. Adding silver training data boosts performance
even further.

提出了一种基于 Neural 的序列到序列的语义解析器，它能够高准确率地产生英语句子的 DRSs，并通过引入 De Bruijn 索引消除变量名以提高解析器性能，以及加入银标注数据来进一步提高性能。