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# Towards JointUD: Part-of-speech Tagging and Lemmatization using Recurrent Neural Networks

**Authors:** Gor Arakelyan, Karen Hambardzumyan and Hrant Khachatrian · **Year:** 2018 · **Venue:** Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies **Link:** [aclanthology.org/K18-2018](https://aclanthology.org/K18-2018/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**6** citations](https://openalex.org/works?filter=cites:W2963412893) openalex · 2026-10-19[**4** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/ud-2018-arakelyan-jointud-part-speech-tagging-lemmatization/) of the 140673 papers whose bibliographies we read[Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies](https://aclanthology.org/volumes/K18-2/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/K18-2018/)
- [PDF](https://aclanthology.org/K18-2018.pdf)
- [DOI 10.18653/v1/k18-2018](https://doi.org/10.18653/v1/k18-2018)
- [arXiv 1809.03211](https://arxiv.org/abs/1809.03211)
- [OpenAlex](https://openalex.org/W2963412893)

**Authors (3)**

1. [Gor Arakelyan](https://openalex.org/A5071264343) [Semantic Scholar](https://www.semanticscholar.org/author/101610005) · h 2 · 10 citations · 3 works
2. [Karen Hambardzumyan](https://openalex.org/A5061387148) [Semantic Scholar](https://www.semanticscholar.org/author/5641536) · h 6 · 671 citations · 18 works
3. [Hrant Khachatrian](https://openalex.org/A5005769994) [ORCID](https://orcid.org/0000-0002-1544-5649) · [Semantic Scholar](https://www.semanticscholar.org/author/1729527) · h 9 · 731 citations · 55 works

Citations and author metrics from openalex (2026-10-19); authors with the same name are not always told apart.

## What it is about

The team's shared-task system uses one LSTM network, trained jointly to output UPOS tags, morphological features and lemmas. Lemmas are produced at character level, which extends a sequence-tagging network. Sentence segmentation, tokenization and dependency parsing come from the UDPipe 1.2 baseline. The model is validated on nine UD v2.2 treebanks, including English EWT and GUM. The authors report that the results are generally not better than the UDPipe baseline, and that the network was not tuned heavily. The work is a first step towards a network that outputs all UD labels.

## Ideas

- I1. `en/src/morph.rs`: a fallback lemmatizer for forms that are missing from the dictionary. It uses suffix edit rules counted from `data/lemmas.tsv` and `data/forms.tsv`. Measure lemma accuracy on EWT test out-of-dictionary forms only, against the dictionary-only route.
- I2. `en morph-eval`: print POS and lemma errors together for each token, to see whether tag errors cause lemma errors. The paper trains the two jointly, so the question is open for us.
- I3. `en/src/ptag.rs`: add suffix-derived number, tense and person features to the tagger, and measure EWT test tagging accuracy with `en tag-eval`, twice with identical results before a claim.

## For Mova

The code is `en/src/morph.rs` and `en/src/ptag.rs`; the chapters are [06. Spelling rules: inflection, derivation, compounds and national standards](https://md.mova.elamur.ai/free/en/docs/languages/en/06-spelling-rules/), `11-nouns.md` and `13-verbs.md`. The paper's lemma accuracy is on UD v2.2, not our UD 2.18, and our dictionary lemmatizer is already at 99.3 to 99.5%, so any gain is in the unknown forms.

Topics: [Part-of-speech tagging](https://md.mova.elamur.ai/free/en/docs/papers/topics/part-of-speech-tagging/)

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/k18-2018](https://doi.org/10.18653/v1/k18-2018) · [arXiv 1809.03211](https://arxiv.org/abs/1809.03211)

```
@inproceedings{arakelyan2018jointud,
  title = {{Towards JointUD: Part-of-speech Tagging and Lemmatization using Recurrent Neural Networks}},
  author = {Arakelyan, Gor and Hambardzumyan, Karen and Khachatrian, Hrant},
  booktitle = {Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies},
  year = {2018},
  doi = {10.18653/v1/k18-2018},
  eprint = {1809.03211},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/k18-2018}
}
```

- [← previous CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies](https://md.mova.elamur.ai/free/en/docs/papers/ud-2017-zeman-conll-2017-shared-task-multilingual/)
- [next → Twitter Universal Dependency Parsing for African-American and Mainstream American English](https://md.mova.elamur.ai/free/en/docs/papers/ud-2018-blodgett-twitter-universal-dependency-parsing-african/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

## References

14 references, 3 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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