Hybrid text simplification using synchronous dependency grammars with hand-written and automatically harvested rules

What it is about

The paper builds a simplification system on synchronous dependency grammars: each rule maps a source tree fragment to a simplified one, with morphology and order changes written into the rule. The system is hybrid. A small set of hand-written rules covers purely syntactic constructions that are hard to learn from data, such as passive to active with tense morphology, and a much larger set of lexicalised rules is harvested automatically from dependency parses of aligned English and Simple English sentences. Generation reuses the word order of the source by default, and the grammar encodes the places where order changes. On 100 Wikipedia sentences with their Simple English counterparts, five raters scored fluency, simplicity and meaning preservation on a five-point scale. The hybrid system was rated significantly better than the quasi-synchronous grammar system of Woodsend and Lapata (p < 0.001) on simplicity and meaning, and significantly better on fluency, with the human simplifications as the upper reference.

Ideas

• I1. Keep two rule tiers apart, as the paper does: a hand-written table for constructions that are hard to learn (passive, tense, order) and an acquired table for word substitutions. In Mova the first tier is the job of en/src/morph.rs and en/src/lin.rs, the second is the lexicon learner in tools/translate/meta.rs. Count how many tokens each tier fixes on dev, run twice.
• I2. Harvest only the lexicalised rules that are unambiguous: the paper keeps a substitution only where it fires in any context. For the translator, the same filter is the author delta rule (at least three tokens fixed, held in at least half of the context).
• I3. Store ordering as a delta from the source order, not as a full order model: only the positions where the target order differs are written, and their cost is measured in bits as in the residual.

For Mova

The synchronous grammar is the closest existing picture of the translator's reader and encoder as one pair of rules, so the design question of the translator (open problem 3) has a worked precedent here. The harvesting step is the lexicon learner of the translator, whose noise problem (open problem 2) this paper handles with an unambiguity filter. Summary only.

Topics: Dependency parsing · Machine translation

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.3115/v1/e14-1076

@inproceedings{siddharthan2014hybrid,
  title = {{Hybrid text simplification using synchronous dependency grammars with hand-written and automatically harvested rules}},
  author = {Siddharthan, Advaith and Mandya, Angrosh},
  booktitle = {Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics},
  year = {2014},
  doi = {10.3115/v1/e14-1076},
  url = {https://doi.org/10.3115/v1/e14-1076}
}

References

31 references, 7 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Sandra M Aluı́sio, Lucia Specia, Thiago AS Pardo, Erick G Maziero, and Renata PM Fortes. 2008. Towards brazilian portuguese automatic text simplification systems. In Proceedings of the eighth ACM symposium on Document engineering, pages 240– 248. ACM. find
2. Richard C. Anderson and Alice Davison. 1988. Conceptual and empirical bases of readibility formulas. In Alice Davison and G. M. Green, editors, Linguistic Complexity and Text Comprehension: Readability Issues Reconsidered. Lawrence Erlbaum Associates, Hillsdale, NJ. find
3. Richard Anderson and Peter Freebody. 1981. Vocabulary knowledge. In John Guthrie, editor, Comprehension and Teaching: Research Reviews, pages 77– 117. International Reading Association, Newark, DE. find
4. R. Barzilay and M. Lapata. 2008. Modeling Local Coherence: An Entity-Based Approach. Computational Linguistics, 34(1):1–34. find
5. R. Barzilay, N. Elhadad, and K. McKeown. 2002. Inferring Strategies for Sentence Ordering in Multi-document News Summarization. Journal of Artificial Intelligence Research, 17(3):35–55. find
6. Isabel L. Beck, Margaret G. McKeown, Gale M. Sinatra, and Jane A. Loxterman. 1991. Revising social studies text from a text-processing perspective: Evidence of improved comprehensibility. Reading Research Quarterly, pages 251–276. find
7. Stefan Bott, Horacio Saggion, and Simon Mille. 2012. Text simplification tools for spanish. In LREC, pages 1665–1671. find
8. Arnaldo Candido Jr, Erick Maziero, Caroline Gasperin, Thiago AS Pardo, Lucia Specia, and Sandra M Aluisio. 2009. Supporting the adaptation of texts for poor literacy readers: a text simplification editor for brazilian portuguese. In Proceedings of the Fourth Workshop on Innovative Use of NLP for Building Educational Applications, pages 34–42. Association for Computational Linguistics. find
9. Yvonne Canning. 2002. Syntactic simplification of Text. Ph.D. thesis, University of Sunderland, UK. find
10. Raman Chandrasekar, Christine Doran, and Bangalore Srinivas. 1996. Motivations and methods for text simplification. In Proceedings of the 16th International Conference on Computational Linguistics (COLING ’96), pages 1041–1044, Copenhagen, Denmark. find
11. William Coster and David Kauchak. 2011. Learning to simplify sentences using wikipedia. In Proceedings of the Workshop on Monolingual Text-To-Text Generation, pages 1–9. Association for Computational Linguistics. find
12. Jan De Belder and Marie-Francine Moens. 2010. Text simplification for children. In Prroceedings of the SIGIR workshop on accessible search systems, pages 19–26. find
13. Yuan Ding and Martha Palmer. 2005. Machine translation using probabilistic synchronous dependency insertion grammars. In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pages 541–548. Association for Computational Linguistics. find our card
14. Mark Dras. 1999. Tree adjoining grammar and the reluctant paraphrasing of text. Ph.D. thesis, Macquarie University NSW 2109 Australia. find
15. J. Kamalski, T. Sanders, and L. Lentz. 2008. Coherence marking, prior knowledge, and comprehension of informative and persuasive texts: Sorting things out. Discourse Processes, 45(4):323–345. find
16. D. Klein and C.D. Manning. 2003. Accurate unlexicalized parsing. In Proceedings of the 41st Annual Meeting on Association for Computational Linguistics-Volume 1, pages 423–430. Association for Computational Linguistics. find our card
17. J.J. L’Allier. 1980. An evaluation study of a computer-based lesson that adjusts reading level by monitoring on task reader characteristics. Ph.D. thesis, University of Minnesota, Minneapolis, MN. find
18. T. Linderholm, M.G. Everson, P. van den Broek, M. Mischinski, A. Crittenden, and J. Samuels. 2000. Effects of Causal Text Revisions on More-and Less-Skilled Readers’ Comprehension of Easy and Difficult Texts. Cognition and Instruction, 18(4):525– 556. find
19. Ani Nenkova, Advaith Siddharthan, and Kathleen McKeown. 2005. Automatically learning cognitive status for multi-document summarization of newswire. In Proceedings of HLT/EMNLP 2005, pages 241–248, Vancouver, Canada. find
20. Advaith Siddharthan and Napoleon Katsos. 2010. Reformulating discourse connectives for non-expert readers. In Proceedings of the 11th Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT 2010), Los Angeles, CA. find
21. Advaith Siddharthan, Ani Nenkova, and Kathleen McKeown. 2011. Information status distinctions and referring expressions: An empirical study of references to people in news summaries. Computational Linguistics, 37(4):811–842. find
22. Advaith Siddharthan. 2002. An architecture for a text simplification system. In Proceedings of the Language Engineering Conference (LEC’02), pages 64– 71, Hyderabad, India. find
23. Advaith Siddharthan. 2003a. Preserving discourse structure when simplifying text. In Proceedings of the European Natural Language Generation Workshop (ENLG), 11th Conference of the European Chapter of the Association for Computational Linguistics (EACL’03), pages 103–110, Budapest, Hungary. find
24. Advaith Siddharthan. 2003b. Resolving pronouns robustly: Plumbing the depths of shallowness. In Proceedings of the Workshop on Computational Treatments of Anaphora, 11th Conference of the European Chapter of the Association for Computational Linguistics (EACL’03), pages 7–14, Budapest, Hungary. find
25. Advaith Siddharthan. 2010. Complex lexico-syntactic reformulation of sentences using typed dependency representations. In Proceedings of the 6th International Natural Language Generation Conference (INLG 2010), Dublin Ireland. find
26. Advaith Siddharthan. 2011. Text simplification using typed dependencies: a comparison of the robustness of different generation strategies. In Proceedings of the 13th European Workshop on Natural Language Generation, pages 2–11. Association for Computational Linguistics. find
27. David A Smith and Jason Eisner. 2006. Quasi-synchronous grammars: Alignment by soft projection of syntactic dependencies. In Proceedings of the Workshop on Statistical Machine Translation, pages 23–30. Association for Computational Linguistics. find our card
28. Kristian Woodsend and Mirella Lapata. 2011. Learning to simplify sentences with quasi-synchronous grammar and integer programming. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 409–420. Association for Computational Linguistics. find our card
29. Sander Wubben, Antal van den Bosch, and Emiel Krahmer. 2012. Sentence simplification by monolingual machine translation. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers-Volume 1, pages 1015–1024. Association for Computational Linguistics. find our card
30. Kenji Yamada and Kevin Knight. 2001. A syntax-based statistical translation model. In Proceedings of the 39th Annual Meeting on Association for Computational Linguistics, pages 523–530. Association for Computational Linguistics. find our card
31. Zhemin Zhu, Delphine Bernhard, and Iryna Gurevych. 2010. A monolingual tree-based translation model for sentence simplification. In Proceedings of the 23rd international conference on computational linguistics, pages 1353–1361. Association for Computational Linguistics. find our card

This page in Markdown → https://md.mova.elamur.ai/free/en/docs/papers/siddharthan-2014-hybrid-text-simplification-synchronous-dependency/