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# ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation

**Authors:** [Kuan-Hao Huang](https://md.mova.elamur.ai/free/en/docs/papers/authors/kuan-hao-huang/), [Varun Iyer](https://md.mova.elamur.ai/free/en/docs/papers/authors/varun-iyer/), I-Hung Hsu, [Anoop Kumar](https://md.mova.elamur.ai/free/en/docs/papers/authors/anoop-kumar/), [Kai-Wei Chang](https://md.mova.elamur.ai/free/en/docs/papers/authors/kai-wei-chang/), [Aram Galstyan](https://md.mova.elamur.ai/free/en/docs/papers/authors/aram-galstyan/) · **Year:** 2023 · **Venue:** ACL 2023 (long paper) **Link:** [aclanthology.org/2023.acl-long.447](https://aclanthology.org/2023.acl-long.447/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**6** citations](https://openalex.org/works?filter=cites:W4385565390) openalex · 2026-10-18[**19** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/huang-2023-paraamr-large-scale-syntactically-diverse/) of the 140673 papers whose bibliographies we read[Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://aclanthology.org/volumes/2023.acl-long/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/2023.acl-long.447/)
- [PDF](https://aclanthology.org/2023.acl-long.447.pdf)
- [DOI 10.18653/v1/2023.acl-long.447](https://doi.org/10.18653/v1/2023.acl-long.447)
- [OpenAlex](https://openalex.org/W4385565390)

**Authors (6)**

1. [Kuan-Hao Huang](https://openalex.org/A5048297682) [ORCID](https://orcid.org/0000-0002-1634-9237) · h 15 · 753 citations · 55 works
2. [Varun Iyer](https://openalex.org/A5036517255) [ORCID](https://orcid.org/0009-0002-7696-1513) · h 5 · 265 citations · 12 works
3. [I-Hung Hsu](https://openalex.org/A5071777193) h 10 · 520 citations · 41 works
4. [Anoop Kumar](https://openalex.org/A5085371439) [ORCID](https://orcid.org/0000-0002-7806-9986) · h 34 · 4317 citations · 323 works
5. [Kai-Wei Chang](https://openalex.org/A5087096372) [ORCID](https://orcid.org/0000-0001-5365-0072) · h 54 · 17628 citations · 433 works
6. [Aram Galstyan](https://openalex.org/A5101715504) [ORCID](https://orcid.org/0000-0003-4215-0886) · h 42 · 12460 citations · 320 works

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

## What it is about

ParaAMR builds about 15 million paraphrase pairs by AMR back-translation. Each source sentence is parsed to an AMR graph; a different node is chosen as the focus (the main assertion); the modified graph is linearised and a sentence is generated from it. Candidates with high perplexity under a language model are filtered out. Compared with back-translated datasets (ParaNMT, ParaBank1 and ParaBank2), the new set is more syntactically diverse by tree-edit distance over constituency parses (top three layers and the whole tree), while semantic similarity, measured with a sentence-embedding model, stays comparable. Human raters agree on the syntactic gain and rate meaning slightly lower. Three uses are tested: sentence embeddings (better STS correlations), syntactically controlled paraphrasing, and few-shot data augmentation.

## Ideas

- I1. Focus change as a frame operation. Choose another predicate of a frame as the head and relinearise. Mova's frames (`prag`) already separate event, fact (link) and say frames with their roles, so a head swap is a counted, deterministic paraphrase generator. It makes a second test set for the translator with the same frame and a different tree. Where: `tools/roundtrip` and `prag`.
- I2. Syntactic diversity beside exact match. Report, in normal mode, a tree-edit distance on UD trees between source and output, next to exact match. The question it answers is whether the translator changes only words or also structure.
- I3. Count-based fluency filter. Replace the neural perplexity filter with a check on the linearisation: reject an order whose class bigrams were never seen in the corpus, with the threshold set on dev. Where: `en/src/lin.rs`.

## For Mova

The focus-change step is the most direct idea here: it is a frame-level operation Mova already has the parts for. The neural paraphraser and the language-model filter are not for Mova; the counted filter and the diversity measure are. The dataset is useful as a measure of diversity; it is not a training source for Mova's own releases.

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/v1/2023.acl-long.447](https://doi.org/10.18653/v1/2023.acl-long.447)

```
@inproceedings{huang2023paraamr,
  title = {{ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation}},
  author = {Huang, Kuan-Hao and Iyer, Varun and Hsu, I-Hung and Kumar, Anoop and Chang, Kai-Wei and Galstyan, Aram},
  booktitle = {Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2023},
  doi = {10.18653/v1/2023.acl-long.447},
  url = {https://doi.org/10.18653/v1/2023.acl-long.447}
}
```

- [← previous Unsupervised Syntactically Controlled Paraphrase Generation with Abstract Meaning Representations](https://md.mova.elamur.ai/free/en/docs/papers/huang-2022-unsupervised-syntactically-controlled-paraphrase-generation/)
- [next → Einleitung zu Aeschylos Agamemnon metrisch übersetzt (Introduction to the verse translation of Aeschylus' Agamemnon)](https://md.mova.elamur.ai/free/en/docs/papers/humboldt-1816-einleitung-zu-aeschylos-agamemnon/)
- [↑ back to Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/mtrans/)

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