---
title: "Neural End-to-end Coreference Resolution for — Schröder"
description: "The paper addresses German coreference resolution, where neural methods had seen little use and no neural results existed for German news corpora."
authors: ["Fynn Schröder", "Hans Ole Hatzel", "Chris Biemann"]
venue: "Proceedings of the 17th Conference on Natural Language Processing (KONVENS 2021)"
source: "https://aclanthology.org/2021.konvens-1.15/"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/coref--schroder-neural-end-end-coreference-resolution/"
---
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# Neural End-to-end Coreference Resolution for German in Different Domains

**Authors:** Fynn Schröder, Hans Ole Hatzel, [Chris Biemann](https://md.research.elamur.ai/free/authors/chris-biemann/) · **Venue:** Proceedings of the 17th Conference on Natural Language Processing (KONVENS 2021) · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/2021.konvens-1.15/) · [PDF](https://aclanthology.org/2021.konvens-1.15.pdf) · [OpenAlex](https://openalex.org/W3213093130)

**Citations:** 9 citations (openalex-snapshot · 2026-10-14) · [10](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
The paper addresses German coreference resolution, where neural methods had seen little use and no neural results existed for German news corpora. The authors adapt the English end-to-end architecture of Lee et al. (2017, 2018), as extended by Joshi et al. (2019) and reimplemented in PyTorch by Xu and Choi (2020). Token representations come from contextual ELECTRA embeddings, trained jointly with mention and entity scoring. They compare two variants that share a mention-proposal step. One is a coarse-to-fine model that links mentions to antecedents. The other is an incremental model that matches mentions against entity representations, so memory grows with the number of entities instead of quadratically with mentions. They evaluate on news data (SemEval-2010, TüBa-D/Z 10.0) and literary data (DROC), and also study language models, pre-training and compute limits. Both variants beat earlier systems by 10–30 CoNLL-F1 points on all three datasets. Error analysis reveals recency bias and inconsistency across long texts. The models and code are released, giving German NLP a strong baseline for news and literary texts.

## Key points
- **Datasets:** TüBa-D/Z 10.0 (no singletons, using Roesiger and Kuhn's splits), SemEval-2010 German (singletons annotated) and DROC.
- **DROC details:** 90 literary chapters averaging about 4,369 tokens each, split 58/14/18. Only character references are annotated, and only mention heads are marked.
- **DROC result:** With singletons, the incremental model reaches 64.72 F1, compared with 53.17 for Krug's (2020) best system (+11.6). The coarse-to-fine model scores 65.50 without singletons. Human agreement is about 82.54 F1.
- **Scaling to long texts:** The incremental model loses more accuracy on long documents than coarse-to-fine. For example, when run on the German-language version of *The Hound of the Baskervilles*, the model scattered mentions of "Holmes" over 31 clusters. Removing the token-distance features reduced recency bias.
- **Availability:** Code and models are at github.com/uhh-lt/neural-coref (branch `konvens`).

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/)

Topics: [Coreference resolution](https://md.research.elamur.ai/free/topics/coreference-resolution/)

## Cited by

Cited by

10 papers of our library cite it (of the 140673 whose bibliographies we read); 2 of them have a page here, 8 not yet.

- [Coreference Resolution through a seq2seq Transition-Based System](https://md.research.elamur.ai/free/papers/bohnet-2023-coreference-resolution-through-seq2seq-transition/)
- [Lions: 1 and Tigers: 2 and Bears: 3, Oh My! Literary Coreference Annotation with LLMs](https://md.research.elamur.ai/free/papers/hicke-2024-lions-1-tigers-2-bears/)

- [← previous Letter 57, to Pammachius, On the Best Method of Translating](https://md.research.elamur.ai/free/papers/jerome-1893-letter-57-pammachius-best/)
- [next → Neural Japanese Zero Anaphora Resolution using Smoothed Large-scale Case Frames with Word Embedding](https://md.research.elamur.ai/free/papers/coref--yamashiro-neural-japanese-zero-anaphora-resolution/)
- [↑ back to undated papers](https://md.research.elamur.ai/free/papers/undated/)

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