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# Modular Neural Machine Translation with a Semantic Pivot - Pilot Study Using AMR

**Authors:** Wenyang Gao, Yaxuan Li, Yunxin Bao, Shulin Huang, [Yue Zhang](https://md.mova.elamur.ai/free/en/docs/papers/authors/yue-zhang/) · **Year:** 2026 · **Venue:** SLiDE workshop (Structured Linguistic Data and Evaluation) **Link:** [aclanthology.org/2026.slide-1.6](https://aclanthology.org/2026.slide-1.6/) · **Licence:** [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) — summary only

[**0** citations](https://openalex.org/works?filter=cites:W7166592880) openalex · 2026-10-18[Proceedings of the Workshop on Structured Linguistic Data and Evaluation (SLiDE)](https://aclanthology.org/volumes/2026.slide-1/)[licence](https://creativecommons.org/licenses/by-nc/4.0/)

- [Publisher's page](https://aclanthology.org/2026.slide-1.6/)
- [PDF](https://aclanthology.org/2026.slide-1.6.pdf)
- [DOI 10.63317/2me4b7kf2d82](https://doi.org/10.63317/2me4b7kf2d82)
- [OpenAlex](https://openalex.org/W7166592880)

**Authors (5)**

1. [Wenyang Gao](https://openalex.org/A5035629029) [ORCID](https://orcid.org/0000-0003-1143-064X) · [Semantic Scholar](https://www.semanticscholar.org/author/2257122746) · h 2 · 55 citations · 9 works
2. [Yaxuan Li](https://openalex.org/A5139552242) [Semantic Scholar](https://www.semanticscholar.org/author/2446318777) · h 0 · 0 citations · 1 works
3. [Yunxin Bao](https://openalex.org/A5139590904) [Semantic Scholar](https://www.semanticscholar.org/author/2446311192) · h 0 · 0 citations · 1 works
4. [Shulin Huang](https://openalex.org/A5129668279) [Semantic Scholar](https://www.semanticscholar.org/author/2347052668) · h 0 · 0 citations · 2 works
5. [Yue Zhang](https://openalex.org/A5124332897) [ORCID](https://orcid.org/0000-0002-5214-2268) · [Semantic Scholar](https://www.semanticscholar.org/author/2311117882) · h 1 · 1 citations · 3 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 builds a translation pipeline with a semantic pivot, in the spirit of the translation pyramid. Each language gets two modules: one that reads a sentence into English AMR, and one that writes the target sentence from AMR. The training data is a silver AMR corpus made by a strong parser from the United Nations Parallel Corpus, for six languages, with about 11.4 million sentences in the full set. The authors compare against a sequence-to-sequence baseline. They report an average gain of about 3 percent in robustness and more than 15 percent in generalisation. They name the parser quality, that is the data quality, as the main bottleneck. The pivot is English throughout, which the authors list as a limit.

## Ideas

- I1. Measure the reader per source language. The authors point to pivot quality as the bottleneck. In Mova the equivalent is the reader's residual per source. Report the Hebrew reader's residual by class (lexical, construction, order), so the weakest layer is visible.
- I2. Swap-in control. Replace one module, reader or writer, by a copy baseline, and measure the loss on the other. Keep the swap in a copy of the repository, measure, then delete it. Do not build it into the translator.
- I3. Data size against quality. Vary the number of verse pairs used to learn the interlingua lexicon, and plot the meta-world's residual against it. The paper's trend, more data helps the pipeline, can then be checked for Mova's lexicon.
- I4. Out-of-book generalisation. Learn the lexicon without one book, then translate that book. The generalisation gap is the Mova analogue of the paper's generalisation test.

## For Mova

The neural modules are not for Mova. The useful part is the modular measurement. Exact mode in `tools/translate` already gives a residual per layer. The swap-in control (I2) and the out-of-book test (I4) are measures for `measure.rs`. They touch open problem 2, a residual-driven lexicon, and open problem 7, the Hebrew reader.

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 10.63317/2me4b7kf2d82](https://doi.org/10.63317/2me4b7kf2d82)

```
@inproceedings{gao2026modular,
  title = {{Modular Neural Machine Translation with a Semantic Pivot - Pilot Study Using AMR}},
  author = {Gao, Wenyang and Li, Yaxuan and Bao, Yunxin and Huang, Shulin and Zhang, Yue},
  booktitle = {Proceedings of the Workshop on Structured Linguistic Data and Evaluation (SLiDE)},
  year = {2026},
  doi = {10.63317/2me4b7kf2d82},
  url = {https://doi.org/10.63317/2me4b7kf2d82}
}
```

- [← previous PPDB: The Paraphrase Database](https://md.mova.elamur.ai/free/en/docs/papers/ganitkevitch-2013-ppdb-paraphrase-database/)
- [next → The WebNLG Challenge: Generating Text from RDF Data](https://md.mova.elamur.ai/free/en/docs/papers/gardent-2017-webnlg-challenge-generating-text-rdf/)
- [↑ back to Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/mtrans/)

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