Papers Text Simplification

ReSyf: a French lexicon with ranked synonyms

In this article, we present ReSyf, a lexical resource of monolingual synonyms ranked according to their difficulty to be read and understood by native learners of French. The synonyms come from an existing lexical networ…

WARP-Text: a Web-Based Tool for Annotating Relationships between Pairs of Texts

We present WARP-Text, an open-source web-based tool for annotating relationships between pairs of texts. WARP-Text supports multi-layer annotation and custom definitions of inter-textual and intra-textual relationships. …

Data-Driven Text Simplification

Explicit Retrofitting of Distributional Word Vectors

Semantic specialization of distributional word vectors, referred to as retrofitting, is a process of fine-tuning word vectors using external lexical knowledge in order to better embed some semantic relation. Existing ret…

Inherent Biases in Reference-based Evaluation for Grammatical Error Correction

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

Learning Simplifications for Specific Target Audiences

Text simplification (TS) is a monolingual text-to-text transformation task where an original (complex) text is transformed into a target (simpler) text. Most recent work is based on sequence-to-sequence neural models sim…

Par4Sim -- Adaptive Paraphrasing for Text Simplification

Learning from a real-world data stream and continuously updating the model without explicit supervision is a new challenge for NLP applications with machine learning components. In this work, we have developed an adaptiv…

Dynamic Multi-Level Multi-Task Learning for Sentence Simplification

Sentence simplification aims to improve readability and understandability, based on several operations such as splitting, deletion, and paraphrasing. However, a valid simplified sentence should also be logically entailed…

Simplification Using Paraphrases and Context-Based Lexical Substitution

Lexical simplification involves identifying complex words or phrases that need to be simplified, and recommending simpler meaning-preserving substitutes that can be more easily understood. We propose a complex word ident…

Discriminating between Lexico-Semantic Relations with the Specialization Tensor Model

We present a simple and effective feed-forward neural architecture for discriminating between lexico-semantic relations (synonymy, antonymy, hypernymy, and meronymy). Our Specialization Tensor Model (STM) simultaneously …

Deep Learning Approaches to Text Production

Text production is a key component of many NLP applications. In data-driven approaches, it is used for instance, to generate dialogue turns from dialogue moves, to verbalise the content of Knowledge bases or to generate …

Predicting misreadings from gaze in children with reading difficulties

We present the first work on predicting reading mistakes in children with reading difficulties based on eye-tracking data from real-world reading teaching. Our approach employs several linguistic and gaze-based features …

Estimating Linguistic Complexity for Science Texts

Evaluation of text difficulty is important both for downstream tasks like text simplification, and for supporting educators in classrooms. Existing work on automated text complexity analysis uses linear models with engin…

LaSTUS/TALN at Complex Word Identification (CWI) 2018 Shared Task

This paper presents the participation of the LaSTUS/TALN team in the Complex Word Identification (CWI) Shared Task 2018 in the English monolingual track . The purpose of the task was to determine if a word in a given sen…

CAMB at CWI Shared Task 2018: Complex Word Identification with Ensemble-Based Voting

This paper presents the winning systems we submitted to the Complex Word Identification Shared Task 2018. We describe our best performing systems{'} implementations and discuss our key findings from this research. Our be…

Complex Word Identification Based on Frequency in a Learner Corpus

We introduce the TMU systems for the Complex Word Identification (CWI) Shared Task 2018. TMU systems use random forest classifiers and regressors whose features are the number of characters, the number of words, and the …

OneStopEnglish corpus: A new corpus for automatic readability assessment and text simplification

This paper describes the collection and compilation of the OneStopEnglish corpus of texts written at three reading levels, and demonstrates its usefulness for through two applications - automatic readability assessment a…

SB@GU at the Complex Word Identification 2018 Shared Task

In this paper, we describe our experiments for the Shared Task on Complex Word Identification (CWI) 2018 (Yimam et al., 2018), hosted by the 13th Workshop on Innovative Use of NLP for Building Educational Applications (B…

Complex Word Identification: Convolutional Neural Network vs. Feature Engineering

We describe the systems of NLP-CIC team that participated in the Complex Word Identification (CWI) 2018 shared task. The shared task aimed to benchmark approaches for identifying complex words in English and other langua…

NILC at CWI 2018: Exploring Feature Engineering and Feature Learning

This paper describes the results of NILC team at CWI 2018. We developed solutions following three approaches: (i) a feature engineering method using lexical, n-gram and psycholinguistic features, (ii) a shallow neural ne…