Text Simplification using Typed Dependencies: A Comparision of the Robustness of Different Generation Strategies

Advaith Siddharthan

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

52 Citations (Scopus)


We present a framework for text simplification based on applying transformation rules to a typed dependency representation produced by the Stanford parser. We test two approaches to regeneration from typed dependencies: (a) gen-light, where the transformed dependency graphs are linearised using the word order and morphology of the original sentence, with any changes coded into the transformation rules, and (b) gen-heavy, where the Stanford dependencies are reduced to a DSyntS representation and sentences are generating formally using the RealPro surface realiser. The main contribution of this paper is to compare the robustness of these approaches in the presence of parsing errors, using both a single parse and an n-best parse setting in an overgenerate and rank approach. We find that the gen-light approach is robust to parser error, particularly in the n-best parse setting. On the other hand, parsing errors cause the realiser in the gen-heavy approach to order words and phrases in ways that are disliked by our evaluators.
Original languageEnglish
Title of host publicationProceedings of the 13th European Workshop on Natural Language Generation
EditorsClaire Gardent, Kristina Striegnitz
PublisherAssociation for Computational Linguistics (ACL)
Number of pages10
Publication statusPublished - Sept 2011
Event13th European Workshop on Natural Language Generation - Nancy, France
Duration: 28 Sept 201130 Sept 2011


Conference13th European Workshop on Natural Language Generation

Bibliographical note

This work was supported by the Economic and Social Research Council (Grant Number RES-000-22-

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