# Weakly Supervised Headline Dependency Parsing

arXiv: https://arxiv.org/abs/2301.10371 · HTML: https://mlsanity.com/paper/2301.10371/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 49 references that are in the map.
- First posted on arXiv 25 Jan 2023.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [A Neural Attention Model for Abstractive Sentence Summarization](https://mlsanity.com/paper/1509.00685/) (2015)
- [Deep Biaffine Attention for Neural Dependency Parsing](https://mlsanity.com/paper/1611.01734/) (2016)
- [75 Languages, 1 Model: Parsing Universal Dependencies Universally](https://mlsanity.com/paper/1904.02099/) (2019)
- [Positional Encoding to Control Output Sequence Length](https://mlsanity.com/paper/1904.07418/) (2019)
- [GoodNewsEveryone: A Corpus of News Headlines Annotated with Emotions, Semantic Roles, and Reader Perception](https://mlsanity.com/paper/1912.03184/) (2019)
- [Parsing Tweets into Universal Dependencies](https://mlsanity.com/paper/1804.08228/) (2018)
- [News Headline Grouping as a Challenging NLU Task](https://mlsanity.com/paper/2105.05391/) (2021)
- [Parser Training with Heterogeneous Treebanks](https://mlsanity.com/paper/1805.05089/) (2018)
- [NSTM: Real-Time Query-Driven News Overview Composition at Bloomberg](https://mlsanity.com/paper/2006.01117/) (2020)
- [Cross-Register Projection for Headline Part of Speech Tagging](https://mlsanity.com/paper/2109.07483/) (2021)

## Abstract (arXiv)

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.