Abstract
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.
Abstract (translated)
英语新闻标题构成一个具有独特语义结构的寄存器,这一结构自20世纪30年代以来就在语言学文献中得到了记录。然而,英语新闻标题在NLP语义解析 community 中却出人意料地得到了较少的关注。我们的目标是通过提供 Universal Dependencies 注释语义依赖树的英语新闻标题语料库,来填补这一差距。这可以帮助我们评估现有的英语新闻标题依赖解析算法的性能。为了改善英语新闻标题的解析精度,我们开发了一种投影方法,从未标记的新闻标题-文章标题引伸出银训练数据。基于银标题解析的训练模型相比于仅基于 gold-annotated 长文本的训练模型,表现出了显著的性能提升。最终,我们发现,虽然投影的银训练数据改善了不同新闻机构之间的parser 性能,但由机构特有的构造子所限制的进步也在其中减弱了。
URL
https://arxiv.org/abs/2301.10371
