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# Reranking and Self-Training for Parser Adaptation

**Authors:** [David McClosky](https://md.mova.elamur.ai/free/en/docs/papers/authors/david-mcclosky/), [Eugene Charniak](https://md.mova.elamur.ai/free/en/docs/papers/authors/eugene-charniak/), [Mark Johnson](https://md.mova.elamur.ai/free/en/docs/papers/authors/mark-johnson/) · **Year:** 2006 · **Venue:** Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL (COLING-ACL 2006) **Link:** [aclanthology.org/P06-1043](https://aclanthology.org/P06-1043/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) — summary only

[**237** citations](https://openalex.org/works?filter=cites:W2138382875) openalex · 2026-10-18[**116** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/aclbulk-2006-mcclosky-reranking-self-training-parser-adaptation/) of the 140673 papers whose bibliographies we read[Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics](https://aclanthology.org/volumes/P06-1/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/P06-1043/)
- [PDF](https://aclanthology.org/P06-1043.pdf)
- [DOI 10.3115/1220175.1220218](https://doi.org/10.3115/1220175.1220218)
- [OpenAlex](https://openalex.org/W2138382875)

**Authors (3)**

1. [David McClosky](https://openalex.org/A5029071304) [ORCID](https://orcid.org/0009-0005-8897-0793) · h 19 · 9378 citations · 50 works
2. [Eugene Charniak](https://openalex.org/A5005380528) h 56 · 13123 citations · 218 works
3. [Mark Johnson](https://openalex.org/A5034461489) [ORCID](https://orcid.org/0000-0003-4809-8441) · h 56 · 23174 citations · 302 works

Citations and author metrics from openalex (2026-10-18); authors with the same name are not always told apart.

## What it is about

Parsers trained on the Wall Street Journal lose much accuracy on other genres: the standard parser scores 89.7% on WSJ but only 82.9% on the Brown corpus. The authors show two techniques that raise Brown accuracy without any labeled Brown data: a reranker over the 50-best parses, which lifts Brown to 85.2%, and self-training on unlabeled out-of-domain text (NANC), where the parser's own reranked parses become new training data, which lifts it to 87.8%. That is a 28% error reduction, and it beats a parser and reranker trained on labeled Brown data (88.4%). The gains come from using unlabeled text from a different domain together with the reranker.

## Ideas

- I1. Self-training on the school corpus: parse the 1.17M-word school corpus with the `en` beam-8 parser, keep the reranked 1-best trees, retrain the perceptron and measure dev LAS on EWT and ESLSpok against the current model (run twice for determinism).
- I2. Reranker for domain shift: add a 50-best reranker (the Charniak and Johnson idea) to the `en` parser and measure LAS on the ATIS queries (current 63.0) and on GENTLE (current 67.9); the paper's gain from 82.9 to 85.2 on Brown is the size to expect.
- I3. Adaptation check for `annot` silver: before a silver corpus is used for training, parse it with the current model and keep only the trees that a reranker prefers, and measure how many silver trees change under the filter.

## For Mova

The self-training loop belongs to the training commands in `en` and the reranker would sit beside `en/src/parse.rs`. The silver corpus is the school corpus under `corpus/`. The chapter that the ideas touch is [08. National varieties](https://md.mova.elamur.ai/free/en/docs/languages/en/08-national-varieties/) and [37. Register and variation](https://md.mova.elamur.ai/free/en/docs/languages/en/37-register-and-variation/). Measure first: EWT dev LAS after one self-training round on the school corpus, compared with the current model.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.3115/1220175.1220218](https://doi.org/10.3115/1220175.1220218)

```
@inproceedings{mcclosky2006reranking,
  title = {{Reranking and Self-Training for Parser Adaptation}},
  author = {McClosky, David and Charniak, Eugene and Johnson, Mark},
  booktitle = {Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics},
  year = {2006},
  doi = {10.3115/1220175.1220218},
  url = {https://doi.org/10.3115/1220175.1220218}
}
```

- [← previous Effective Self-Training for Parsing](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2006-mcclosky-effective-self-training-parsing/)
- [next → Learning Accurate, Compact, and Interpretable Tree Annotation](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2006-petrov-learning-accurate-compact-interpretable-tree/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

## References

16 references, 5 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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