---
title: "The Change that Matters in Discourse Parsing — Atwell 2022"
description: "Discourse parsers trained on the small, mostly news-based treebanks degrade sharply on out-of-domain text."
authors: ["Katherine Atwell", "Anthony Sicilia", "Seong Jae Hwang", "Malihe Alikhani"]
year: 2022
venue: "Findings of the Association for Computational Linguistics: ACL 2022"
source: "https://aclanthology.org/2022.findings-acl.68/"
doi: "10.18653/v1/2022.findings-acl.68"
arxiv: "2203.11317"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/atwell-2022-change-that-matters-discourse-parsing/"
---
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# The Change that Matters in Discourse Parsing: Estimating the Impact of Domain Shift on Parser Error

**Authors:** [Katherine Atwell](https://md.research.elamur.ai/free/authors/katherine-atwell/), Anthony Sicilia, Seong Jae Hwang, [Malihe Alikhani](https://md.research.elamur.ai/free/authors/malihe-alikhani/) · **Year:** 2022 · **Venue:** Findings of the Association for Computational Linguistics: ACL 2022 · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2022.findings-acl.68/) · [PDF](https://aclanthology.org/2022.findings-acl.68.pdf) · [DOI 10.18653/v1/2022.findings-acl.68](https://doi.org/10.18653/v1/2022.findings-acl.68) · [arXiv 2203.11317](https://arxiv.org/abs/2203.11317) · [OpenAlex](https://openalex.org/W4285119343)

**Citations:** 11 citations (openalex-snapshot · 2026-10-14) · [16](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Discourse parsers trained on the small, mostly news-based treebanks degrade sharply on out-of-domain text. Practitioners therefore need a way to anticipate how much a model will lose when it moves to a new domain. The authors argue that standard two-sample statistics measuring feature-distribution shift are a poor proxy for this loss, the error-gap, because shift and error can diverge in either direction. They propose the h-discrepancy instead. This classifier-dependent statistic generalizes the source-guided discrepancy of Kuroki et al. (2019). They derive upper and lower bounds on its bias as an estimator of error-gap. They then run more than 2400 transfer experiments across six English PDTB- and RST-style corpora, using BERT-based argument-pair encodings with linear and fully-connected classifiers. The h-discrepancy tracks error-gap better than two-sample statistics. A regression analysis of its estimation error also shows which models, features, and datasets transfer well. The released code and Python package support model and dataset selection under domain shift.

## Key points
- **Datasets:** PDTB 2.0, PDTB 3.0, RST-DT, GUM, TED-MDB and BioDRB, covering news, biomedical text, TED talks, Reddit posts and fiction.
- **Theory:** The bias bounds imply that a small h-discrepancy usually means transfer from train to test set is safe.
- **Weak shift statistics:** Two-sample shift statistics correlate only weakly with error-gap, especially for RST-DT-framework data and non-news targets.
- **Features:**
  - S-BERT works best when train and test sets are similar.
  - A-BERT is preferable as they diverge.
  - P-BERT differs little from A-BERT.
- **Classifiers and targets:**
  - Fully-connected networks slightly beat linear classifiers, but this advantage fades with larger discrepancy.
  - At high discrepancy, non-news targets are slightly easier to adapt to than news targets.
  - GUM is the hardest adaptation setting and RST-DT the easiest.
- **Resources:** Code is available at github.com/anthonysicilia/change-that-matters-ACL2022.

*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: [Discourse analysis](https://md.research.elamur.ai/free/topics/discourse-analysis/)

## Cited by

Cited by

16 papers of our library cite it (of the 140673 whose bibliographies we read); 2 of them have a page here, 14 not yet.

- [The DISRPT 2023 Shared Task on Elementary Discourse Unit Segmentation, Connective Detection, and Relation Classification](https://md.research.elamur.ai/free/papers/braud-2023-disrpt-2023-shared-task-elementary/)
- [Why Can't Discourse Parsing Generalize? A Thorough Investigation of the Impact of Data Diversity](https://md.research.elamur.ai/free/papers/liu-2023-why-can-t-discourse-parsing/)

## Cite

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

```bibtex
@inproceedings{atwell2022change,
  title = {{The Change that Matters in Discourse Parsing: Estimating the Impact of Domain Shift on Parser Error}},
  author = {Atwell, Katherine and Sicilia, Anthony and Hwang, Seong Jae and Alikhani, Malihe},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2022},
  year = {2022},
  doi = {10.18653/v1/2022.findings-acl.68},
  eprint = {2203.11317},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2022.findings-acl.68}
}
```

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- [↑ back to papers of 2022](https://md.research.elamur.ai/free/papers/2022/page/3/)

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