---
title: "Data Augmentation for Morphological — Silfverberg 2017"
description: "This paper presents the CU Boulder Linguistics submission to the 2017 CoNLL-SIGMORPHON Shared Task, which focused on universal morphological reinflection."
authors: ["Miikka Silfverberg", "Adam Wiemerslage", "Ling Liu", "Lingshuang Jack Mao"]
year: 2017
venue: "Proceedings of the CoNLL SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection"
source: "https://aclanthology.org/K17-2010/"
doi: "10.18653/v1/k17-2010"
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/silfverberg-2017-data-augmentation-morphological-reinflection/"
---
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# Data Augmentation for Morphological Reinflection

**Authors:** [Miikka Silfverberg](https://md.research.elamur.ai/free/authors/miikka-silfverberg/), Adam Wiemerslage, [Ling Liu](https://md.research.elamur.ai/free/authors/ling-liu/), Lingshuang Jack Mao · **Year:** 2017 · **Venue:** Proceedings of the CoNLL SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/K17-2010/) · [PDF](https://aclanthology.org/K17-2010.pdf) · [DOI 10.18653/v1/k17-2010](https://doi.org/10.18653/v1/k17-2010) · [OpenAlex](https://openalex.org/W2740149041)

**Citations:** 73 citations (openalex-snapshot · 2026-10-14) · [49](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
This paper presents the CU Boulder Linguistics submission to the 2017 CoNLL-SIGMORPHON Shared Task, which focused on universal morphological reinflection. Its 52 languages, Ukrainian among them, come with training sets of between 100 and roughly 10,000 examples. Neural reinflection models do very well with abundant data but collapse when only a few hundred examples are available, because they overfit.

The authors build a character-level bidirectional LSTM Encoder-Decoder with attention, closely modeled on the 2016 winning system by Kann and Schütze. Their main contribution is in training. They create synthetic examples by locating the longest common substring of a lemma and its inflected form, treating it as an approximate stem, and replacing it in both strings with random characters. They also combine ten models through weighted voting and add a copy symbol for characters unseen in training, though the copy symbol was used in Task 2 only.

In Task 1 (inflecting a lemma from a feature set), the system clearly beats the non-neural baseline, even in the low-data setting. In Task 2 (paradigm completion), it overfits and performs poorly. The work shows that simple augmentation can make neural reinflection workable for under-resourced languages.

## Key points
- Each subtask has low, medium and high data settings. For Task 1 these are 100, 1,000 and 10,000 examples. For Task 2 they are roughly 10, 50 and 200 paradigms.
- According to the authors, an unmodified Kann and Schütze-style model scores only about 0–1% accuracy for most languages when trained on 100 examples.
- Accuracy in the low setting varies widely: Basque reaches 4% and Danish 68.90%. Lower scores go with more distinct feature combinations in the test data. Only 8% of Basque test feature sets appear in the low training data, compared with 99% in the high setting, where Basque reaches 100%.
- In the low setting, the system falls below the baseline on eight languages. Most gaps are small, but Navajo trails by 7.3 points. The authors attribute this to short shared substrings between lemma and form (2.9 characters on average, versus 6.7 for Danish), which make augmentation less effective.
- Generating replacement stems with language models instead of random characters lowered accuracy, probably because the synthetic strings resembled real training data too closely.

*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/)

## Cited by

Cited by

49 papers of our library cite it (of the 140673 whose bibliographies we read); 3 of them have a page here, 46 not yet.

- [Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations](https://md.research.elamur.ai/free/papers/kobayashi-2018-contextual-augmentation-data-augmentation-words/)
- [Counting the Bugs in ChatGPT's Wugs: A Multilingual Investigation into the Morphological Capabilities of a Large Language Model](https://md.research.elamur.ai/free/papers/lex-2023-weissweiler-chatgpt-wugs-morphology/)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://md.research.elamur.ai/free/papers/wei-2019-eda-easy-data-augmentation-techniques/)

## Cite

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

```bibtex
@inproceedings{silfverberg2017data,
  title = {{Data Augmentation for Morphological Reinflection}},
  author = {Silfverberg, Miikka and Wiemerslage, Adam and Liu, Ling and Mao, Lingshuang Jack},
  booktitle = {Proceedings of the CoNLL SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection},
  year = {2017},
  doi = {10.18653/v1/k17-2010},
  url = {https://doi.org/10.18653/v1/k17-2010}
}
```

- [← previous Data Augmentation for Low-Resource Neural Machine Translation](https://md.research.elamur.ai/free/papers/fadaee-2017-data-augmentation-low-resource-neural/)
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- [↑ back to papers of 2017](https://md.research.elamur.ai/free/papers/2017/)

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