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# Generating Text from Uniform Meaning Representation

**Authors:** Emma Markle, Reihaneh Iranmanesh, [Shira Wein](https://md.mova.elamur.ai/free/en/docs/papers/authors/shira-wein/) · **Year:** 2025 · **Venue:** IJCNLP-AACL 2025 (14th International Joint Conference on NLP and 4th Asia-Pacific Chapter of ACL) **Link:** [aclanthology.org/2025.ijcnlp-long.16](https://aclanthology.org/2025.ijcnlp-long.16/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**0** citations](https://openalex.org/works?filter=cites:W7138092579) openalex · 2026-10-18[**1** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/markle-2025-generating-text-uniform-meaning-representation/) of the 140673 papers whose bibliographies we read[Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics](https://aclanthology.org/volumes/2025.ijcnlp-long/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/2025.ijcnlp-long.16/)
- [PDF](https://aclanthology.org/2025.ijcnlp-long.16.pdf)
- [DOI 10.18653/v1/2025.ijcnlp-long.16](https://doi.org/10.18653/v1/2025.ijcnlp-long.16)
- [arXiv 2502.11973](https://arxiv.org/abs/2502.11973)
- [OpenAlex](https://openalex.org/W7138092579)

**Authors (3)**

1. [Emma Markle](https://openalex.org/A5116335555) h 1 · 1 citations · 5 works
2. [Reihaneh Iranmanesh](https://openalex.org/A5116335556) h 1 · 2 citations · 11 works
3. [Shira Wein](https://openalex.org/A5075058724) [ORCID](https://orcid.org/0000-0002-1062-0866) · h 6 · 80 citations · 54 works

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

## What it is about

UMR is a successor of AMR with document-level information, aspect, modality and coreference, across several languages. The paper builds the first UMR-to-text generators on a small data set (about 1,470 training sentences over six languages, with English and Chinese the main focus). Three approaches are tried: passing UMR straight to AMR-to-text models (the output fills with UMR terms such as "singular" and "3rd person"); converting UMR to AMR by rules and then generating (fluent but with lost person and number information, Smatch 0.63 on held-out pairs); and fine-tuning AMR-to-text models and large multilingual models on UMR. The best fine-tuned models reach multilingual BERTScore 0.825 for English and 0.882 for Chinese. Human judges rate the pipeline most fluent, and find that the fine-tuned models keep document-level information useful even for single sentences. Indigenous-language output is judged unusable.

## Ideas

- I1. Strip UMR-only attributes at the reader (refer-number, refer-person, aspect) and keep them as features of meta tokens. This is open problem 5: number and person from UD FEATS. Test that merges differing only in these features are split.
- I2. Term-leak gate: count output tokens that are annotation labels rather than English words. Zero is required; include one injected leak as a negative control.
- I3. Pronoun default: the converter maps every third-person pronoun to "they", and the pipeline cannot recover the right pronoun. Count pronoun errors per author, since this is a style delta candidate rather than a fixed rule.
- I4. Measure the converter itself before any generation claim: the paper reports Smatch against gold for its UMR-to-AMR step. For Mova, the equivalent is the round-trip divergence of each reader change.

## For Mova

The ideas fit `tools/translate/meta.rs` and `profile.rs` (features, pronouns), with the round-trip check in `tools/roundtrip`. Fine-tuning large models is not for Mova; the lesson is that document context helped even at sentence level, which can be tested as extra context features in the encoder.

Topics: [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/)

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/2025.ijcnlp-long.16](https://doi.org/10.18653/v1/2025.ijcnlp-long.16) · [arXiv 2502.11973](https://arxiv.org/abs/2502.11973)

```
@inproceedings{markle2025generating,
  title = {{Generating Text from Uniform Meaning Representation}},
  author = {Markle, Emma and Iranmanesh, Reihaneh and Wein, Shira},
  booktitle = {Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics},
  year = {2025},
  doi = {10.18653/v1/2025.ijcnlp-long.16},
  eprint = {2502.11973},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2025.ijcnlp-long.16}
}
```

- [← previous The Multidimensional Quality Metrics (MQM) Framework: a new framework for translation quality assessment](https://md.mova.elamur.ai/free/en/docs/papers/mariana-2015-multidimensional-quality-metrics-mqm-framework/)
- [next → Automatic induction of shallow-transfer rules for open-source machine translation](https://md.mova.elamur.ai/free/en/docs/papers/martinez-2007-automatic-induction-shallow-transfer-rules/)
- [↑ back to Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/mtrans/)

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