Exploring Neural Text Simplification Models
نویسندگان
چکیده
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.
منابع مشابه
SimpleNets: Quality Estimation with Resource-Light Neural Networks
We introduce SimpleNets: a resource-light solution to the sentence-level Quality Estimation task of WMT16 that combines Recurrent Neural Networks, word embedding models, and the principle of compositionality. The SimpleNets systems explore the idea that the quality of a translation can be derived from the quality of its n-grams. This approach has been successfully employed in Text Simplificatio...
متن کاملNeural Machine Translation from Simplified Translations
Text simplification aims at reducing the lexical, grammatical and structural complexity of a text while keeping the same meaning. In the context of machine translation, we introduce the idea of simplified translations in order to boost the learning ability of deep neural translation models. We conduct preliminary experiments showing that translation complexity is actually reduced in a translati...
متن کاملAn Experimental Study of LSTM Encoder-Decoder Model for Text Simplification
Text simplification (TS) aims to reduce the lexical and structural complexity of a text, while still retaining the semantic meaning. Current automatic TS techniques are limited to either lexical-level applications or manually defining a large amount of rules. Since deep neural networks are powerful models that have achieved excellent performance over many difficult tasks, in this paper, we prop...
متن کاملSimple PPDB: A Paraphrase Database for Simplification
We release the Simple Paraphrase Database, a subset of of the Paraphrase Database (PPDB) adapted for the task of text simplification. We train a supervised model to associate simplification scores with each phrase pair, producing rankings competitive with state-of-theart lexical simplification models. Our new simplification database contains 4.4 million paraphrase rules, making it the largest a...
متن کاملA Semantic Relevance Based Neural Network for Text Summarization and Text Simplification
ive text summarization has achieved successful performance thanks to the sequence-to-sequence model (Sutskever, Vinyals, and Le 2014) and attention mechanism (Bahdanau, Cho, and Bengio 2014). Rush, Chopra, and Weston (2015) first used an attention-based encoder to compress texts and a neural network language decoder to generate summaries. Following this work, recurrent encoder was introduced to...
متن کامل