Papers Text Simplification

Phrase-Level Metaphor Identification Using Distributed Representations of Word Meaning

Metaphor is an essential element of human cognition which is often used to express ideas and emotions that might be difficult to express using literal language. Processing metaphoric language is a challenging task for a …

Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources

Word vector specialisation (also known as retrofitting) is a portable, light-weight approach to fine-tuning arbitrary distributional word vector spaces by injecting external knowledge from rich lexical resources such as …

Word Embedding Approach for Synonym Extraction of Multi-Word Terms

Korean L2 Vocabulary Prediction: Can a Large Annotated Corpus be Used to Train Better Models for Predicting Unknown Words?

Crowdsourced Corpus of Sentence Simplification with Core Vocabulary

Simplified Corpus with Core Vocabulary

DeModify: A Dataset for Analyzing Contextual Constraints on Modifier Deletion

ETPC - A Paraphrase Identification Corpus Annotated with Extended Paraphrase Typology and Negation

A Leveled Reading Corpus of Modern Standard Arabic

A Detailed Evaluation of Neural Sequence-to-Sequence Models for In-domain and Cross-domain Text Simplification

CEFR-based Lexical Simplification Dataset

Text Simplification from Professionally Produced Corpora

CATS: A Tool for Customized Alignment of Text Simplification Corpora

SimPA: A Sentence-Level Simplification Corpus for the Public Administration Domain

Utilisation d'une base de connaissances de sp\'ecialit\'e et de sens commun pour la simplification de comptes-rendus radiologiques (Radiological text simplification using a general knowledge base)

Dans le domaine m{\'e}dical, la simplification des textes est {\`a} la fois une t{\^a}che souhaitable pour les patients et scientifiquement stimulante pour le domaine du traitement automatique du langage naturel. En effe…

A comparative study of word embeddings and other features for lexical complexity detection in French

Lexical complexity detection is an important step for automatic text simplification which serves to make informed lexical substitutions. In this study, we experiment with word embeddings for measuring the complexity of F…

Inherent Biases in Reference based Evaluation for Grammatical Error Correction and Text Simplification

The prevalent use of too few references for evaluating text-to-text generation is known to bias estimates of their quality ({\it low coverage bias} or LCB). This paper shows that overcoming LCB in Grammatical Error Corre…

Sentence Simplification with Memory-Augmented Neural Networks

Sentence simplification aims to simplify the content and structure of complex sentences, and thus make them easier to interpret for human readers, and easier to process for downstream NLP applications. Recent advances in…

Query and Output: Generating Words by Querying Distributed Word Representations for Paraphrase Generation

Most recent approaches use the sequence-to-sequence model for paraphrase generation. The existing sequence-to-sequence model tends to memorize the words and the patterns in the training dataset instead of learning the me…

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

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 …