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# Extending a Parser to Distant Domains Using a Few Dozen Partially Annotated Examples

**Authors:** Vidur Joshi, Matthew Peters, [Mark Hopkins](https://md.mova.elamur.ai/free/en/docs/papers/authors/mark-hopkins/) · **Year:** 2018 · **Venue:** Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) **Link:** [aclanthology.org/P18-1110](https://aclanthology.org/P18-1110/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**103** citations](https://openalex.org/works?filter=cites:W2799222909) openalex · 2026-10-18[**39** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/acl-2018-joshi-extending-parser-distant-domains-few/) of the 140673 papers whose bibliographies we read[Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://aclanthology.org/volumes/P18-1/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/P18-1110/)
- [PDF](https://aclanthology.org/P18-1110.pdf)
- [DOI 10.18653/v1/p18-1110](https://doi.org/10.18653/v1/p18-1110)
- [OpenAlex](https://openalex.org/W2799222909)

**Authors (3)**

1. [Vidur Joshi](https://openalex.org/A5017963313) h 4 · 791 citations · 5 works
2. [Matthew Peters](https://openalex.org/A5101509445) [ORCID](https://orcid.org/0000-0002-2105-2585) · h 27 · 20606 citations · 53 works
3. Mark Hopkins

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

## What it is about

The paper revisits domain adaptation for parsers. First, a constituency parser (the Reconciled Span Parser, a span-classification model with contextual word representations and no POS input) trained on the Wall Street Journal reaches 94.3 F1 on the WSJ test set and over 90 F1 on the Brown corpus without any Brown training. For syntactically distant domains, the model is trained directly on partial annotations: a few dozen sentences where annotators mark only some constituents. On geometry text, the share of error-free parses rises from 45.2% to 72.6% with about five dozen annotated sentences, with no drop on newswire. The key design choice is that the model's probability unit matches the annotation unit (is this span a constituent), so partial annotation needs no change to the training procedure.

## Ideas

- I1. `gparse` training: use partial arc annotations (only the arcs a human or teacher has confirmed, with the rest masked out of the loss) and measure LAS on EWT dev with 50, 100 and 200 partially labelled sentences against full training; the paper's claim is that partial labels recover most of the domain gain.
- I2. `en parse-eval` domain curve: train the `en` perceptron on EWT, test on a distant domain (ATIS, lowercase queries, LAS 63.0 now) and add a few dozen corrected sentences from that domain; report LAS per corrected sentence, as the paper reports error-free share.
- I3. Report the share of error-free test sentences (sentence exact match) next to LAS in `en parse-eval`; the paper uses it as the practical measure, and it is more sensitive to the few errors a user sees.

## For Mova

The chapters are [20. Syntax: the Universal Dependencies frame](https://md.mova.elamur.ai/free/en/docs/languages/en/20-syntax-overview/) and `21-clause-structure.md`. The code is `gparse` (the arc scorer and its training masks) and `en/src/parse.rs` for the perceptron. The idea fits Mova's rule that examples are not truth: a partially checked sentence is a safer training unit than a full silver tree. Measure on a held-out domain, not on the training data.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/p18-1110](https://doi.org/10.18653/v1/p18-1110)

```
@inproceedings{joshi2018extending,
  title = {{Extending a Parser to Distant Domains Using a Few Dozen Partially Annotated Examples}},
  author = {Joshi, Vidur and Peters, Matthew and Hopkins, Mark},
  booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2018},
  doi = {10.18653/v1/p18-1110},
  url = {https://doi.org/10.18653/v1/p18-1110}
}
```

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