# A Cross-Genre Ensemble Approach to Robust Reddit Part of Speech Tagging

arXiv: https://arxiv.org/abs/2004.14312 · HTML: https://mlsanity.com/paper/2004.14312/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2020.

## Built on by (most cited first, 9 of 9)

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- [Are UD Treebanks Getting More Consistent? A Report Card for English UD](https://mlsanity.com/paper/2302.00636/) (2023)
- [Unifying the Scope of Bridging Anaphora Types in English: Bridging Annotations in ARRAU and GUM](https://mlsanity.com/paper/2410.01170/) (2024)
- [DiscoExplorer: An Open Interface for the Study of Multilingual Discourse Relations](https://mlsanity.com/paper/2605.15304/) (2026)
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## Abstract (arXiv)

Part of speech tagging is a fundamental NLP task often regarded as solved for high-resource languages such as English. Current state-of-the-art models have achieved high accuracy, especially on the news domain. However, when these models are applied to other corpora with different genres, and especially user-generated data from the Web, we see substantial drops in performance. In this work, we study how a state-of-the-art tagging model trained on different genres performs on Web content from unfiltered Reddit forum discussions. More specifically, we use data from multiple sources: OntoNotes, a large benchmark corpus with 'well-edited' text, the English Web Treebank with 5 Web genres, and GUM, with 7 further genres other than Reddit. We report the results when training on different splits of the data, tested on Reddit. Our results show that even small amounts of in-domain data can outperform the contribution of data an order of magnitude larger coming from other Web domains. To make progress on out-of-domain tagging, we also evaluate an ensemble approach using multiple single-genre taggers as input features to a meta-classifier. We present state of the art performance on tagging Reddit data, as well as error analysis of the results of these models, and offer a typology of the most common error types among them, broken down by training corpus.