Unsupervised Sentence Simplification Using Deep Semantics

What it is about

The system simplifies sentences with neither hand-written rules nor an aligned training corpus. It parses each sentence into a discourse representation structure (DRS), a deep semantic graph whose nodes are events and entities, and applies substitution, reordering, splitting and deletion on that graph, not on words or phrase trees. Splitting points are chosen from events: a probabilistic split-feature table over thematic role sets scores each candidate split, which is compared with the same sentence unsplit. Output candidates are ranked by a language model for grammaticality. On the test set of Zhu et al. (2010) and on human judgements, the unsupervised system is reported as competitive with four supervised systems. The paper argues that splitting on semantic distinctions needs fewer rules than splitting on syntactic ones, since relative and appositive clauses can map to one semantic category.

Ideas

• I1. Split on events, not on tokens. Count the main events (predicates) in a frame. Propose a split when a sentence has more than one event and the events share no argument, a rule that uses the frame structure Mova already builds (prag).
• I2. Split-feature table as counts. Count which role patterns occur at split points in simple text, and use the counts as the split rule. Measure reader error on the split sentences, so the rule is judged by its effect on the translator.
• I3. Shared-argument test. Whether two events share an entity is a count check on the shared entities of the frames in world. Use it to decide whether a long sentence goes to the target as one unit or as two.

For Mova

The sentence-splitting logic belongs in tools/roundtrip and prag, not in the translator's encoder. The event count and the shared-argument test are both deterministic and cheap. The language-model ranker is not for Mova; the counted split table is. Relevant to open problem 3 (encoder writes trees) for the structure of split output.

Topics: Machine translation

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.18653/v1/w16-6620

@inproceedings{narayan2016unsupervised,
  title = {{Unsupervised Sentence Simplification Using Deep Semantics}},
  author = {Narayan, Shashi and Gardent, Claire},
  booktitle = {Proceedings of the 9th International Natural Language Generation conference},
  year = {2016},
  doi = {10.18653/v1/w16-6620},
  url = {https://doi.org/10.18653/v1/w16-6620}
}

References

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