A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplification

Mounica Maddela, Wei Xu

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Abstract
Current lexical simplification approaches rely heavily on heuristics and corpus level features that do not always align with human judgment. We create a human-rated word-complexity lexicon of 15,000 English words and propose a novel neural readability ranking model with a Gaussian-based feature vectorization layer that utilizes these human ratings to measure the complexity of any given word or phrase. Our model performs better than the state-of-the-art systems for different lexical simplification tasks and evaluation datasets. Additionally, we also produce SimplePPDB++, a lexical resource of over 10 million simplifying paraphrase rules, by applying our model to the Paraphrase Database (PPDB).
Anthology ID:
D18-1410
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3749–3760
Language:
URL:
https://aclanthology.org/D18-1410
DOI:
10.18653/v1/D18-1410
Bibkey:
Cite (ACL):
Mounica Maddela and Wei Xu. 2018. A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplification. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3749–3760, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplification (Maddela & Xu, EMNLP 2018)
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PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/D18-1410.pdf
Video:
 https://preview.aclanthology.org/teach-a-man-to-fish/D18-1410.mp4
Code
 mounicam/lexical_simplification