---
title: "Transferring Coreference Resolvers with — Martins 2015"
description: "Coreference resolvers usually depend on annotated corpora, which most languages lack."
authors: ["André F. T. Martins"]
year: 2015
venue: "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)"
source: "https://aclanthology.org/P15-1138/"
doi: "10.3115/v1/p15-1138"
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/martins-2015-transferring-coreference-resolvers-posterior-regularization/"
---
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# Transferring Coreference Resolvers with Posterior Regularization

**Authors:** [André F. T. Martins](https://md.research.elamur.ai/free/authors/andre-f-t-martins/) · **Year:** 2015 · **Venue:** Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · **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/P15-1138/) · [PDF](https://aclanthology.org/P15-1138.pdf) · [DOI 10.3115/v1/p15-1138](https://doi.org/10.3115/v1/p15-1138) · [OpenAlex](https://openalex.org/W2251533216)

**Citations:** 22 citations (openalex-snapshot · 2026-10-14) · [13](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Coreference resolvers usually depend on annotated corpora, which most languages lack. This paper builds resolvers for low-resource languages by transferring knowledge from an English system, using English→Spanish and English→Brazilian Portuguese as test cases. First, the Berkeley Coreference Resolution system labels the English side of the trilingual FAPESP parallel corpus. Next, the predicted mentions and chains are carried over to the target side through Berkeley aligner word alignments, which produces a noisy training set. A mention-ranking model with latent coreference trees is then trained on this projected data. Training uses a new softmax-margin version of posterior regularization (PR), which adds a task-specific cost to tune the precision/recall balance. It also uses a penalized formulation that sets the Lagrange multipliers directly, avoiding a saddle-point optimization. The approach outperforms delexicalized transfer with bilingual embeddings, direct bitext projection, and vanilla PR. Its largest gains are in recall. This matters because it yields a full end-to-end resolver without any target-language coreference annotation.

## Key points
- **Data:** The FAPESP corpus (Aziz and Specia, 2011) was used for projection. Evaluation used AnCora (SemEval 2010) for Spanish and Summ-It 3.0 for Portuguese (50 Folha de São Paulo documents). English OntoNotes (CoNLL 2011) served as the source training data, and TurboParser supplied POS tags and parses.
- **Projection filtering:**
  - Alignments with confidence below 0.95 were dropped.
  - Mentions were projected with a maximal-span heuristic.
  - Documents were discarded if more than 15% of their mentions were unaligned.
  - About 73.8% of mentions survived for Portuguese and 69.9% for Spanish.
- **Mention recall (Spanish dev set):** Softmax-margin PR reached 47.1%. This is roughly 5 points above direct projection, 10 above delexicalized transfer, and about 5 above vanilla PR. The higher mention recall carried over into recall gains on MUC, B³, and CEAF_e.
- **Possessive pronouns (Spanish):** The share wrongly treated as non-anaphoric was:
  - 53.3% for the delexicalized model
  - 30.1% for direct projection
  - 4.9% for vanilla PR
  - 3.4% for softmax-margin PR

*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

13 papers of our library cite it (of the 140673 whose bibliographies we read); 4 of them have a page here, 9 not yet.

- [Investigating Multilingual Coreference Resolution by Universal Annotations](https://md.research.elamur.ai/free/papers/chai-2023-investigating-multilingual-coreference-resolution-universal/)
- [Multi-source annotation projection of coreference chains: assessing strategies and testing opportunities](https://md.research.elamur.ai/free/papers/grishina-2017-multi-source-annotation-projection-coreference/)
- [Bridging Resolution: A Survey of the State of the Art](https://md.research.elamur.ai/free/papers/kobayashi-2020-bridging-resolution-survey-state-art/)
- [Projection-based Coreference Resolution Using Deep Syntax](https://md.research.elamur.ai/free/papers/novak-2017-projection-based-coreference-resolution-deep/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{martins2015transferring,
  title = {{Transferring Coreference Resolvers with Posterior Regularization}},
  author = {Martins, André F. T.},
  booktitle = {Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  year = {2015},
  doi = {10.3115/v1/p15-1138},
  url = {https://doi.org/10.3115/v1/p15-1138}
}
```

- [← previous Towards Universal Paraphrastic Sentence Embeddings](https://md.research.elamur.ai/free/papers/wieting-2015-towards-universal-paraphrastic-sentence-embeddings/)
- [next → Transition-Based Dependency Parsing with Stack Long Short-Term Memory](https://md.research.elamur.ai/free/papers/missing-2015-dyer-transition-based-dependency-parsing-stack/)
- [↑ back to papers of 2015](https://md.research.elamur.ai/free/papers/2015/page/2/)

## References

53 references, 32 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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