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# A Pointer Network Architecture for Joint Morphological Segmentation and Tagging

**Authors:** [Amit Seker](https://md.mova.elamur.ai/free/en/docs/papers/authors/amit-seker/); [Reut Tsarfaty](https://md.mova.elamur.ai/free/en/docs/papers/authors/reut-tsarfaty/) · **Year:** 2020 · **Venue:** Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 4368–4378 **Link:** [aclanthology.org/2020.findings-emnlp.391](https://aclanthology.org/2020.findings-emnlp.391/) · library `ud-2020-seker-pointer-network-architecture-joint-morphological` · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) (ACL Anthology)

[**9** citations](https://openalex.org/works?filter=cites:W3098265177) openalex · 2026-10-18[**12** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/ud-2020-seker-pointer-network-architecture-joint-morphological/) of the 140673 papers whose bibliographies we read[Findings of the Association for Computational Linguistics: EMNLP 2020](https://aclanthology.org/volumes/2020.findings-emnlp/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/2020.findings-emnlp.391/)
- [PDF](https://aclanthology.org/2020.findings-emnlp.391.pdf)
- [DOI 10.18653/v1/2020.findings-emnlp.391](https://doi.org/10.18653/v1/2020.findings-emnlp.391)
- [OpenAlex](https://openalex.org/W3098265177)

**Authors (2)**

1. [Amit Seker](https://openalex.org/A5049561362) h 7 · 185 citations · 13 works
2. [Reut Tsarfaty](https://openalex.org/A5063283689) [ORCID](https://orcid.org/0000-0001-6406-6568) · h 24 · 2886 citations · 217 works

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

## What it is about

Morphological disambiguation (MD) picks one decomposition of each token into morphemes. The authors feed a symbolic lattice of all analyses per token (HebLex for Hebrew, TRMorph2 for Turkish) into a neural pointer network. The lattice is flattened into a sequence of analyses, each analysis is embedded by averaging its morpheme properties, and the decoder points at one analysis per token with copy attention. On the CoNLL18 UD test sets, with infused lattices (gold path forced in), Hebrew joint segmentation and tagging reached aligned-segment F1 94.41 and Turkish 96.6, both at or above the shared-task leaders. With the realistic, uninfused lattices, Hebrew reached 91.3, level with the leader's 91.36. On the SPMRL Hebrew treebank the model beat the earlier non-neural MoreMD on all settings (test uninfused 90.49 against 87.53). Hebrew averages 10 analyses per token against 2.6 in Turkish, and of 850 wrong predictions in Hebrew, 330 had no correct analysis in the lattice.

## Ideas

- I1. Split every wrong prediction into two kinds: "gold analysis missing from the candidate list" and "gold analysis present but not chosen". The paper's 330 of 850 counts are the template. Add this split to the `tools/hbo` evaluation so that segmenter coverage and ranking are reported apart.
- I2. A small choice model over the segmenter's candidates: the `tools/hbo` tables already give the candidate splits. A tiny scorer (linear, or a rule with a few context features such as neighbour UPOS and prefix class) picks one split per token. Compare it with the current rules by cross-validation on train, not on test.
- I3. Store the candidates per token as an ordered list, so that a choice is a single index. Log the index with the features that decided it. This matches Mova's "say why" rule and fits the `world` store.
- I4. Write the copy-attention idea down as a rule: the decoder may only choose inside the analyses of the current token. In Mova this is a constraint on the segmenter, not a learned mask.

## For Mova

I1 is a change to the evaluation in `tools/hbo`. I2 and I4 are for the segmenter in `tools/hbo` (tables `lex`, `pre`). I3 is for `world`, and the tool notes are in [13. Biblical Hebrew in Mova: the tools](https://md.mova.elamur.ai/free/en/docs/languages/hbo/13-mova-tools/). The neural part is not for Mova; only the lattice-choice framing is.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/2020.findings-emnlp.391](https://doi.org/10.18653/v1/2020.findings-emnlp.391)

```
@inproceedings{seker2020pointer,
  title = {{A Pointer Network Architecture for Joint Morphological Segmentation and Tagging}},
  author = {Seker, Amit and Tsarfaty, Reut},
  booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2020},
  year = {2020},
  doi = {10.18653/v1/2020.findings-emnlp.391},
  url = {https://doi.org/10.18653/v1/2020.findings-emnlp.391}
}
```

- [← previous Universal Morpho-Syntactic Parsing and the Contribution of Lexica: Analyzing the ONLP Lab Submission to the CoNLL 2018 Shared Task](https://md.mova.elamur.ai/free/en/docs/papers/ud-2018-seker-universal-morpho-syntactic-parsing-contribution/)
- [next → ParaShoot: A Hebrew Question Answering Dataset](https://md.mova.elamur.ai/free/en/docs/papers/ud-2021-keren-parashoot-hebrew-question-answering-dataset/)
- [↑ back to Modern Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/he/)

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