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# Semantic Neural Machine Translation Using AMR

**Authors:** [Linfeng Song](https://md.mova.elamur.ai/free/en/docs/papers/authors/linfeng-song/), [Daniel Gildea](https://md.mova.elamur.ai/free/en/docs/papers/authors/daniel-gildea/), [Yue Zhang](https://md.mova.elamur.ai/free/en/docs/papers/authors/yue-zhang/), [Zhiguo Wang](https://md.mova.elamur.ai/free/en/docs/papers/authors/zhiguo-wang/), Jinsong Su · **Year:** 2019 · **Venue:** Transactions of the ACL, vol. 7 **Link:** [aclanthology.org/Q19-1002](https://aclanthology.org/Q19-1002/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**143** citations](https://openalex.org/works?filter=cites:W2916721475) openalex · 2026-10-18[**74** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/song-2019-semantic-neural-machine-translation-amr/) of the 140673 papers whose bibliographies we read[Transactions of the Association for Computational Linguistics](https://aclanthology.org/volumes/Q19-1/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/Q19-1002/)
- [PDF](https://aclanthology.org/Q19-1002.pdf)
- [DOI 10.1162/tacl_a_00252](https://doi.org/10.1162/tacl_a_00252)
- [OpenAlex](https://openalex.org/W2916721475)

**Authors (5)**

1. Linfeng Song
2. Daniel Gildea
3. Yue Zhang
4. Zhiguo Wang
5. Jinsong Su

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 asks whether an AMR graph of the source sentence helps neural machine translation. The model keeps a BiLSTM encoder over the words and adds a graph recurrent network that encodes the AMR graph without linearising it. A doubly attentive LSTM decoder reads both encodings. On WMT16 English-German (4.17M sentence pairs, automatically parsed AMRs) the AMR model reaches 25.5 BLEU against 23.7 for a strong attention baseline. On a 226K-pair subset the gap is larger, 19.2 against 16.0, which the authors read as a data-sparsity effect. Gains hold across sentence-length buckets, and the authors expected them to be largest for long sentences. In a human study on 100 test sentences, the AMR model wins on 46, the baseline on 23, and 31 are ties, judged on predicate-argument structure.

## Ideas

- I1. Keep the graph. The paper finds that a graph encoder beats a serialised AMR. The meta-world in `tools/translate` is already a graph with heads and relations. Test whether the encoder can read head and relation directly, instead of a flat sequence, and report the residual per class.
- I2. Gain against data size. The gain is larger when data is scarce. Subsample the verse-aligned Bible pairs to several sizes, and measure the meta-world's gain over copying at each size. This tests whether structure helps Mova where it has little data.
- I3. Length buckets. Report exact match and residual by sentence length, as the paper does with BLEU. The hypothesis that long-range structure matters can then be checked on verses of different lengths.
- I4. Count-based human check. The win, loss and tie judgment on predicate-argument structure can be replaced by a count: the share of meta tokens whose predicate and argument roles survive a round trip through `tools/roundtrip`.

## For Mova

The neural encoder and decoder are not for Mova. The principle carries over: a graph of predicate-argument structure helps most where data is thin. The count-based forms in I1 and I4 use `tools/translate` (`meta.rs`, `measure.rs`) and `tools/roundtrip`. They address open problem 1, reader attachment noise, and open problem 4 on the role of function words.

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 10.1162/tacl_a_00252](https://doi.org/10.1162/tacl_a_00252)

```
@article{song2019semantic,
  title = {{Semantic Neural Machine Translation Using AMR}},
  author = {Song, Linfeng and Gildea, Daniel and Zhang, Yue and Wang, Zhiguo and Su, Jinsong},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2019},
  doi = {10.1162/tacl\_a\_00252},
  url = {https://doi.org/10.1162/tacl_a_00252}
}
```

- [← previous AMR-to-text Generation with Synchronous Node Replacement Grammar](https://md.mova.elamur.ai/free/en/docs/papers/song-2017-amr-text-generation-synchronous-node/)
- [next → Blast: A Tool for Error Analysis of Machine Translation Output](https://md.mova.elamur.ai/free/en/docs/papers/stymne-2011-blast-tool-error-analysis-machine/)
- [↑ back to Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/mtrans/)

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