6533b7d6fe1ef96bd126727b
RESEARCH PRODUCT
Attention-based Model for Evaluating the Complexity of Sentences in English Language
Giovanni PilatoGiosuè Lo BoscoDaniele Schicchisubject
050101 languages & linguisticsComputer scienceText simplificationcomputer.software_genredeep-learningNLPDeep Learning0501 psychology and cognitive sciencestext simplificationBaseline (configuration management)Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniSettore INF/01 - InformaticaArtificial neural networktext-complexity-evaluationbusiness.industryDeep learning05 social sciences050301 educationExtension (predicate logic)AutomationAutomatic Text SimplificationSupport vector machineArtificial intelligencebusiness0503 educationcomputerNatural language processingSentencedescription
The automation of text complexity evaluation (ATCE) is an emerging problem which has been tackled by means of different methodologies. We present an effective deep learning- based solution which leverages both Recurrent Neural and the Attention mechanism. The developed system is capable of classifying sentences written in the English language by analysing their syntactical and lexical complexity. An accurate test phase has been carried out, and the system has been compared with a baseline tool based on the Support Vector Machine. This paper represents an extension of a previous deep learning model, which allows showing the suitability of Neural Networks to evaluate sentence complexity in two different languages: Italian and English.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2020-06-01 |