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# Grammar-Based Concept Alignment for Domain-Specific Machine Translation

**Authors:** [Arianna Masciolini](https://md.mova.elamur.ai/free/en/docs/papers/authors/arianna-masciolini/), [Aarne Ranta](https://md.mova.elamur.ai/free/en/docs/papers/authors/aarne-ranta/) · **Year:** 2021 · **Venue:** CNL 2020/21 (Seventh International Workshop on Controlled Natural Language) **Link:** [aclanthology.org/2021.cnl-1.2](https://aclanthology.org/2021.cnl-1.2/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**2** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/masciolini-2021-grammar-based-concept-alignment-domain/) of the 140673 papers whose bibliographies we read[Proceedings of the Seventh International Workshop on Controlled Natural Language (CNL 2020/21)](https://aclanthology.org/volumes/2021.cnl-1/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/2021.cnl-1.2/)
- [PDF](https://aclanthology.org/2021.cnl-1.2.pdf)

**Authors (2)**

1. Arianna Masciolini
2. Aarne Ranta

Citations and author metrics from OpenAlex (); authors with the same name are not always told apart.

## What it is about

The paper proposes concept alignment (CA) from parallel sentences in a grammar-based way, to build the translation lexicon of a domain-specific GF system. It takes UD trees for both sides of a sentence pair, and aligns subtrees recursively: a pair is aligned if its roots have the same dependency label, with a backup rule on matching open-class POS multisets, and with the heads of matched subtrees also added so that one-word correspondences are not lost. Known translation divergences are handled explicitly, such as an adverb translated as a prepositional phrase. The aligned pairs are converted into GF lexicon rules by the gf-ud tool. On 100 sentences of the Parallel UD corpus, the alignments were correct for 77 percent (English-Italian) and 85 percent (English-Swedish) of distinct pairs, against 42 and 64 percent for the fast_align statistical aligner trained on 1,000 sentences. On raw course-plan text, parsed with UDPipe, correctness was 63 to 66 percent. A small MT test on generated sentences then used the lexicon.

## Ideas

- I1. Align the trees of the two languages for each parallel verse, with priority-ordered criteria (same label first, then POS match, then head alignment). This is an alternative to the longest-common-subsequence anchors of meta.rs, and it can be measured by counting merges and checking a hand-labelled sample against the current lexicon.
- I2. Keep a table of known divergences as data: adverb against oblique, direct object against oblique. Each rule has a hit count, and an author's constructions (such as "it came to pass" against "it happened") can be added as one more row instead of as code.
- I3. Separate pair-specific alignments from alignments usable for a standard. The paper's example, an idiom that mentions a different object in each language, is correct for the pair but must not enter the lexicon. Mova's merge needs the same label, so an idiomatic merge stays with its author.
- I4. Use an open-class POS-multiset check as a cheap test on each proposed merge, as the paper does. It would catch merges of the kind be with have, where function words are removed before the comparison.

## For Mova

The code is tools/translate (meta.rs for merges and anchors, hebrew.rs for the tree alignment with MACULA trees) and tools/roundtrip for the trees. The alignment criteria and the divergence table fit open problems 1 and 2: the lexicon keeps a merge only if the trees agree. The paper's precision figures are for a different task and are not a target for Mova; the criterion that a merge should be usable in a standard is the part to copy.

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{masciolini2021grammar,
  title = {{Grammar-Based Concept Alignment for Domain-Specific Machine Translation}},
  author = {Masciolini, Arianna and Ranta, Aarne},
  booktitle = {Proceedings of the Seventh International Workshop on Controlled Natural Language (CNL 2020/21)},
  year = {2021},
  url = {https://aclanthology.org/2021.cnl-1.2/}
}
```

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## References

14 references, 3 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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