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# Exploiting Task Reversibility of DRS Parsing and Generation: Challenges and Insights from a Multi-lingual Perspective

**Authors:** Muhammad Saad Amin, Luca Anselma, Alessandro Mazzei · **Year:** 2025 · **Venue:** LoResLM 2025 (First Workshop on Language Models for Low-Resource Languages) **Link:** [aclanthology.org/2025.loreslm-1.22](https://aclanthology.org/2025.loreslm-1.22/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**0** citations](https://openalex.org/works?filter=cites:W7216946005) openalex · 2026-10-18[**1** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/amin-2025-exploiting-task-reversibility-drs-parsing/) of the 140673 papers whose bibliographies we read[Proceedings of the First Workshop on Language Models for Low-Resource Languages](https://aclanthology.org/volumes/2025.loreslm-1/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/2025.loreslm-1.22/)
- [PDF](https://aclanthology.org/2025.loreslm-1.22.pdf)
- [OpenAlex](https://openalex.org/W7216946005)

**Authors (3)**

1. [Muhammad Saad Amin](https://openalex.org/A5008746407) [ORCID](https://orcid.org/0000-0002-7002-9373) · h 9 · 231 citations · 27 works
2. [Luca Anselma](https://openalex.org/A5043355817) [ORCID](https://orcid.org/0000-0003-2292-6480) · h 13 · 654 citations · 115 works
3. [Alessandro Mazzei](https://openalex.org/A5013143796) [ORCID](https://orcid.org/0000-0003-3072-0108) · h 16 · 861 citations · 155 works

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

## What it is about

The paper studies error dynamics in two pipelines built on the same reversible task. Parse-Generate-Parse (PGP) takes text to DRS, back to text, and back to DRS; Generate-Parse-Generate (GPG) takes DRS to text, back to DRS, and back to text. Pre-trained byT5 models are used without retraining, on the Parallel Meaning Bank for English (high-resource), Italian (mid-resource) and Urdu (low-resource). Each example is labelled as improved (error corrected), unchanged or worsened (error amplified). The authors find that the pipelines more often propagate errors than correct them. Urdu is the most sensitive, Italian shows a moderate effect, and English is the most stable. Parsing scores are highest in English (SMATCH F1 93.56 without the pipeline, 93.06 with it).

## Ideas

- I1. Use Mova's own stack for the same test: meta-world to text and back (GPG), and text to meta-world, to text and back (PGP). Label each example improved, unchanged or worse, as in the paper, and run twice for identical output.
- I2. Count error propagation by class: which divergence classes (lexical, construction, order, function word) grow across a round trip. A class that grows is the layer to fix first.
- I3. Language sensitivity: the paper finds the low-resource language most fragile. The Hebrew reader is the weakest part of the translator, so expect the same ordering there, and measure it with the same classes per source language.
- I4. Count-based augmentation: the paper multiplies its training data with name, lexical and grammatical substitutions. A cheap version on the Bible pairs swaps names and tenses by counts, then checks whether the round trip gets more stable. Measure on dev only.

## For Mova

The round-trip test fits `tools/roundtrip` and `tools/translate`, with the classes the translator's residual already uses. The byT5 models are not for Mova. The finding that errors are more often propagated than corrected is a reason to measure the round trip before adding layers.

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.

```
@inproceedings{amin2025exploiting,
  title = {{Exploiting Task Reversibility of DRS Parsing and Generation: Challenges and Insights from a Multi-lingual Perspective}},
  author = {Amin, Muhammad Saad and Anselma, Luca and Mazzei, Alessandro},
  booktitle = {Proceedings of the First Workshop on Language Models for Low-Resource Languages},
  year = {2025},
  url = {https://aclanthology.org/2025.loreslm-1.22/}
}
```

- [← previous Translation by Quasi Logical Form Transfer](https://md.mova.elamur.ai/free/en/docs/papers/alshawi-1991-translation-quasi-logical-form-transfer/)
- [next → ACES: Translation Accuracy Challenge Sets for Evaluating Machine Translation Metrics](https://md.mova.elamur.ai/free/en/docs/papers/amrhein-2022-aces-translation-accuracy-challenge-sets/)
- [↑ back to Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/mtrans/)

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