publication . Conference object . 2017

Learning How to Simplify From Explicit Labeling of Complex-Simplified Text Pairs

Alva-Manchego, Fernando; Bingel, Joachim; Paetzold, Gustavo Henrique; Scarton, Carolina; Specia, Lucia;
Open Access
  • Published: 27 Nov 2017
Abstract
Current research in text simplification has been hampered by two central problems: (i) the small amount of high-quality parallel simplification data available, and (ii) the lack of explicit annotations of simplification operations, such as deletions or substitutions, on existing data. While the recently introduced Newsela corpus has alleviated the first problem, simplifications still need to be learned directly from parallel text using black-box, end-to-end approaches rather than from explicit annotations. These complex-simple parallel sentence pairs often differ to such a high degree that generalization becomes difficult. End-to-end models also make it hard to interpret what is actually learned from data. We propose a method that decomposes the task of TS into its sub-problems. We devise a way to automatically identify operations in a parallel corpus and introduce a sequence-labeling approach based on these annotations. Finally, we provide insights on the types of transformations that different approaches can model.
Funded by
EC| SIMPATICO
Project
SIMPATICO
SIMplifying the interaction with Public Administration Through Information technology for Citizens and cOmpanies
  • Funder: European Commission (EC)
  • Project Code: 692819
  • Funding stream: H2020 | RIA
Validated by funder
Download fromView all 4 versions
Open Access
ZENODO
Conference object . 2017
Providers: ZENODO
1 research outcomes, page 1 of 1
Any information missing or wrong?Report an Issue