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# A Monolingual Tree-based Translation Model for Sentence Simplification

**Authors:** Zhemin Zhu, [Delphine Bernhard](https://md.mova.elamur.ai/free/en/docs/papers/authors/delphine-bernhard/), [Iryna Gurevych](https://md.mova.elamur.ai/free/en/docs/papers/authors/iryna-gurevych/) · **Year:** 2010 · **Venue:** Coling 2010 (23rd International Conference on Computational Linguistics) **Link:** [aclanthology.org/C10-1152](https://aclanthology.org/C10-1152/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) — summary only

[**236** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/zhu-2010-monolingual-tree-based-translation-model/) of the 140673 papers whose bibliographies we read[Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)](https://aclanthology.org/volumes/C10-1/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/C10-1152/)
- [PDF](https://aclanthology.org/C10-1152.pdf)

**Authors (3)**

1. Zhemin Zhu
2. Delphine Bernhard
3. Iryna Gurevych

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

## What it is about

The paper treats sentence simplification as translation from complex to simple sentences, with the complex parse tree as the source. Its Tree-based Simplification Model (TSM) covers four operations in one model: splitting, dropping, reordering and substitution. Each operation is driven by a learned feature table over tree nodes, such as a node's constituent label and its children, and the probabilities of all operation sequences that yield the same output are summed. To train it, the authors build PWKP, a parallel corpus of Wikipedia and Simple Wikipedia sentences, by aligning article pairs at sentence level with TF-IDF similarity. Evaluation uses BLEU, NIST and a readability formula; the model scores better on readability than the baseline systems. The work is the reference point for later tree-based simplifiers in this group of papers.

## Ideas

- I1. Operations as counted tables. Write the four operations as counted tables keyed by UD relation and head POS, for example how often a relative clause is dropped when one Bible translation is read into the meta-world and another is produced. This keeps the model transparent and deterministic. Where: `tools/translate` (reader and encoder) and `tools/roundtrip`.
- I2. Alignment fallback. When a corpus has no verse numbering, align sentences by TF-IDF similarity before counting. Mova's current Bible pairs are verse-aligned, so this is a fallback for texts such as the school and story corpora.
- I3. Split decision as a tree-feature table. Decide split points from node features, then complete the copied material. Use the split table as the reader's check on split sentences in the meta-world.

## For Mova

The dropping and reordering tables are the counted form of the operations the translator needs when an author removes or moves material. The split-and-complete step fits `tools/roundtrip` and the encoder's chunking (open problem 3). The model itself is not for Mova; the counted tables and the verse-free alignment fallback are.

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{zhu2010monolingual,
  title = {{A Monolingual Tree-based Translation Model for Sentence Simplification}},
  author = {Zhu, Zhemin and Bernhard, Delphine and Gurevych, Iryna},
  booktitle = {Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)},
  year = {2010},
  url = {https://aclanthology.org/C10-1152/}
}
```

- [← previous Handling Syntactic Divergence in Low-resource Machine Translation](https://md.mova.elamur.ai/free/en/docs/papers/zhou-2019-handling-syntactic-divergence-low-resource/)
- [↑ back to Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/mtrans/)

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