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Simple and Effective Text Simplification Using Semantic and Neural Methods

What it is about

The paper adds a semantic splitting step before a neural machine translation simplifier. Direct Semantic Splitting (DSS) parses each sentence with a UCCA semantic parser, which gives scenes (events with their participants), and splits the sentence at its main scenes using two simple rules, one for parallel scenes and one for elaborator scenes. The split sentences are then passed to a neural simplifier. The motivation is that plain neural MT simplifiers are over-conservative and often return the input unchanged. The method is compared with phrase-based and neural baselines by BLEU, SARI and a copy measure (the share of output sentences identical to the input), and by human ratings of grammaticality, meaning and simplicity. The paper reports the combined system as competitive with the state of the art on joint lexical and structural simplification. The paper also proposes SAMSA, a structural score that compares scenes of source and output, which is the basis of its structural evaluation.

Ideas

For Mova

The split rule (I1) and the copy rate (I2) fit tools/translate and tools/roundtrip directly. The UCCA parser is not used in Mova, and the neural simplifier is not for Mova; the scene counts and the structural score are. The structural score gives a second measure for open problem 3, the encoder writing trees.

Topics: Machine translation

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.18653/v1/p18-1016

@inproceedings{sulem2018simple,
  title = {{Simple and Effective Text Simplification Using Semantic and Neural Methods}},
  author = {Sulem, Elior and Abend, Omri and Rappoport, Ari},
  booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2018},
  doi = {10.18653/v1/p18-1016},
  url = {https://doi.org/10.18653/v1/p18-1016}
}

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