# How Universal is Genre in Universal Dependencies?

arXiv: https://arxiv.org/abs/2112.04971 · HTML: https://mlsanity.com/paper/2112.04971/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 58 references that are in the map.
- First posted on arXiv 9 Dec 2021.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach](https://mlsanity.com/paper/1909.00161/) (2019)
- [Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection](https://mlsanity.com/paper/2004.10643/) (2020)
- [Unsupervised Domain Clusters in Pretrained Language Models](https://mlsanity.com/paper/2004.02105/) (2020)
- [Learning to select data for transfer learning with Bayesian Optimization](https://mlsanity.com/paper/1707.05246/) (2017)
- [What to do about non-standard (or non-canonical) language in NLP](https://mlsanity.com/paper/1608.07836/) (2016)
- [Cost-effective Selection of Pretraining Data: A Case Study of Pretraining BERT on Social Media](https://mlsanity.com/paper/2010.01150/) (2020)
- [Universal Dependencies for Learner English](https://mlsanity.com/paper/1605.04278/) (2016)
- [82 Treebanks, 34 Models: Universal Dependency Parsing with Multi-Treebank Models](https://mlsanity.com/paper/1809.02237/) (2018)
- [Genre as Weak Supervision for Cross-lingual Dependency Parsing](https://mlsanity.com/paper/2109.04733/) (2021)
- [Parsing Natural Language Sentences by Semi-supervised Methods](https://mlsanity.com/paper/1506.04897/) (2015)
- [The Persian Dependency Treebank Made Universal](https://mlsanity.com/paper/2009.10205/) (2020)

## Built on by (most cited first, 4 of 4)

- [A Second Wave of UD Hebrew Treebanking and Cross-Domain Parsing](https://mlsanity.com/paper/2210.07873/) (2022)
- [MaiBaam: A Multi-Dialectal Bavarian Universal Dependency Treebank](https://mlsanity.com/paper/2403.10293/) (2024)
- [Out-of-Domain Evaluation of Finnish Dependency Parsing](https://mlsanity.com/paper/2204.10621/) (2022)
- [Can Humans Identify Domains?](https://mlsanity.com/paper/2404.01785/) (2024)

## Abstract (arXiv)

This work provides the first in-depth analysis of genre in Universal Dependencies (UD). In contrast to prior work on genre identification which uses small sets of well-defined labels in mono-/bilingual setups, UD contains 18 genres with varying degrees of specificity spread across 114 languages. As most treebanks are labeled with multiple genres while lacking annotations about which instances belong to which genre, we propose four methods for predicting instance-level genre using weak supervision from treebank metadata. The proposed methods recover instance-level genre better than competitive baselines as measured on a subset of UD with labeled instances and adhere better to the global expected distribution. Our analysis sheds light on prior work using UD genre metadata for treebank selection, finding that metadata alone are a noisy signal and must be disentangled within treebanks before it can be universally applied.