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Broad Coverage Multilingual Deep Sentence Generation with a Stochastic Multi-Level Realizer

What it is about

The paper describes a multilingual generator that starts from deep, predicate-argument-like structures and produces sentences in several languages. It works in stages, and each stage has its own statistical decoder built from support vector machines. One decoder maps the semantic level to the syntactic level, one orders the words, and one produces the morphology. The stages are trained on multi-level annotated corpora. The authors report a BLEU score in the range of roughly 0.61 to 0.69 across the languages, with the highest value for Chinese, and they compare it with a grammar-based realiser on related inputs. They also observe that current annotation schemes do not always serve generation well, and that the corpora need extra generation-oriented layers.

Ideas

For Mova

Only the principle carries over: a deep generator in stages, each stage scored on its own. The fit is tools/translate (profile.rs, the residual split) and en/src/lin.rs. The paper's SVM method is not for Mova, and a count-based form of the same stages is what Mova can use.

Topics: Machine translation

Cite

The paper itself, not this card. BibTeX from the library's record of the work.

@inproceedings{bohnet2010broad,
  title = {{Broad Coverage Multilingual Deep Sentence Generation with a Stochastic Multi-Level Realizer}},
  author = {Bohnet, Bernd and Wanner, Leo and Mille, Simon and Burga, Alicia},
  booktitle = {Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)},
  year = {2010},
  url = {https://aclanthology.org/C10-1012/}
}

References

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