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# Comparing Neural Network Parsers for a Less-resourced and Morphologically-rich Language: Amharic Dependency Parser

**Authors:** [Binyam Ephrem Seyoum](https://md.mova.elamur.ai/free/en/docs/papers/authors/binyam-ephrem-seyoum/); [Yusuke Miyao](https://md.mova.elamur.ai/free/en/docs/papers/authors/yusuke-miyao/); [Baye Yimam Mekonnen](https://md.mova.elamur.ai/free/en/docs/papers/authors/baye-yimam-mekonnen/) · **Year:** 2020 · **Venue:** Proceedings of the first workshop on Resources for African Indigenous Languages **Link:** [aclanthology.org/2020.rail-1.5](https://aclanthology.org/2020.rail-1.5/) · library `aclbulk-2020-seyoum-comparing-neural-network-parsers-less` · **Licence:** [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) (ELRA, LREC proceedings) — summary only

[**2** citations](https://openalex.org/works?filter=cites:W3088148744) openalex-snapshot · 2026-10-14[Proceedings of the first workshop on Resources for African Indigenous Languages](https://aclanthology.org/volumes/2020.rail-1/)[licence](https://creativecommons.org/licenses/by-nc/4.0/)

- [Publisher's page](https://aclanthology.org/2020.rail-1.5/)
- [PDF](https://aclanthology.org/2020.rail-1.5.pdf)
- [OpenAlex](https://openalex.org/W3088148744)

**Authors (3)**

1. [Binyam Ephrem Seyoum](https://openalex.org/A5057620892) h 5 · 65 citations · 10 works
2. [Yusuke Miyao](https://openalex.org/A5004444958) [ORCID](https://orcid.org/0000-0002-0678-3400) · h 35 · 4512 citations · 263 works
3. Baye Yimam Mekonnen

Citations and author metrics from openalex-snapshot (2026-10-14); authors with the same name are not always told apart.

## What it is about

The paper compares four off-the-shelf neural dependency parsers (UDPipe, jPTDP, UUParser, Turku) on the Amharic UD treebank, which has about 1,074 manually annotated sentences. Evaluation is ten-fold cross-validation, since the data is too small for a fixed split. The best result is UDPipe with a LAS of 83.79, rising to 85.26 when pretrained fastText vectors are added. The authors then run the pipeline from raw text: word segmentation drops from about 98% to about 80%, tagging falls from 93–95% to 74–77% with predicted segmentation, and attachment falls with it. Their conclusion is that the segmentation of clitics, done before tagging, is the bottleneck for a small-data parser, and that a joint model with morphological tags is a promising direction.

## Ideas

- I1. Report the parser in two numbers: with gold segmentation and from raw text, with the drop attributed to each stage. For `tools/hbo` and `gparse`, measure LAS on the MACULA or PTNK trees with gold segments and with the segmenter's output, so that a loss is traced to the segmenter and not to the parser.
- I2. Use ten-fold cross-validation on the small Hebrew treebanks (the same sentences in every fold, grouped by book or source) as the decision protocol for `gparse` changes, so that a small held-out set is not spent on every comparison.
- I3. Character models help where the type-token ratio is high (the paper's jPTDP gain is largest on this kind of language): test a character-level input for the unpointed Hebrew path, against the word-only input, on the same folds.

## For Mova

I1 is a reporting rule for `tools/hbo` (segmenter then `gparse`) and for `treebanks/hbo/ptnk`. I2 is an evaluation protocol for `gparse` in `treebanks/hbo/`. I3 is an experiment for the unpointed mode of `tools/hbo`, and a reason to keep the segmenter's tables visible in its output. The paper is non-commercial: only the summary above is used, and nothing from the text or the treebank is carried into a release.

Topics: [Dependency parsing](https://md.mova.elamur.ai/free/en/docs/papers/topics/dependency-parsing/) · [Treebanks](https://md.mova.elamur.ai/free/en/docs/papers/topics/treebanks/)

## Cite

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

```
@inproceedings{seyoum2020comparing,
  title = {{Comparing Neural Network Parsers for a Less-resourced and Morphologically-rich Language: Amharic Dependency Parser}},
  author = {Seyoum, Binyam Ephrem and Miyao, Yusuke and Mekonnen, Baye Yimam},
  booktitle = {Proceedings of the first workshop on Resources for African Indigenous Languages},
  year = {2020},
  url = {https://aclanthology.org/2020.rail-1.5/}
}
```

- [← previous On the Importance of Tokenization in Arabic Embedding Models](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2020-alkaoud-importance-tokenization-arabic-embedding-models/)
- [next → Transfer Learning for Less-Resourced Semitic Languages Speech Recognition: the Case of Amharic](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2020-woldemariam-transfer-learning-less-resourced-semitic/)
- [↑ back to Semitic languages](https://md.mova.elamur.ai/free/en/docs/papers/sem/)

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