Exploring Neural Text Simplification Models
| Autor: | Liviu P. Dinu, Simone Paolo Ponzetto, Sanja Štajner, Sergiu Nisioi |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Sequence
Lexical simplification Artificial neural network Text simplification business.industry Computer science 02 engineering and technology computer.software_genre Reduction (complexity) 030507 speech-language pathology & audiology 03 medical and health sciences 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Grammaticality Artificial intelligence 0305 other medical science business computer Natural language processing |
| Zdroj: | ACL (2) |
| Popis: | We present the first attempt at using sequence to sequence neural networks to model text simplification (TS). Unlike the previously proposed automated TS systems, our neural text simplification (NTS) systems are able to simultaneously perform lexical simplification and content reduction. An extensive human evaluation of the output has shown that NTS systems achieve almost perfect grammaticality and meaning preservation of output sentences and higher level of simplification than the state-of-the-art automated TS systems. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|