In this paper, we present a new parallel corpus addressed to researchers, teachers, and speech therapists interested in text simplification as a means of alleviating difficulties in children learning to read. The corpus is composed of excerpts drawn from 79 authentic literary (tales, stories) and scientific (documentary) texts commonly used in French schools for children aged between 7 to 9 years old. The excerpts were manually simplified at the lexical, morpho-syntactic, and discourse levels in order to propose a parallel corpus for reading tests and for the development of automatic text simplification tools. A sample of 21 poor-reading and dyslexic children with an average reading delay of 2.5 years read a portion of the corpus. The transcripts of readings errors were integrated into the corpus with the goal of identifying lexical difficulty in the target population. By means of statistical testing, we provide evidence that the manual simplifications significantly reduced reading errors, highlighting that the words targeted for simplification were not only well-chosen but also substituted with substantially easier alternatives. The entire corpus is available for consultation through a web interface and available on demand for research purposes.

The objective of this work is to introduce text simplification as a potential reading aid to help improve the poor reading performance experienced by visually impaired individuals. As a first step, we explore what makes a text especially complex when read with low vision, by assessing the individual effect of three word properties (frequency, orthographic similarity and length) on reading speed in the presence of Central visual Field Loss (CFL). Individuals with bilateral CFL induced by macular diseases read pairs of French sentences displayed with the self-paced reading method. For each sentence pair, sentence n contained a target word matched with a synonym word of the same length included in sentence n+1. Reading time was recorded for each target word. Given the corpus we used, our results show that (1) word frequency has a significant effect on reading time (the more frequent the faster the reading speed) with larger amplitude (in the range of seconds) compared to normal vision; (2) word neighborhood size has a significant effect on reading time (the more neighbors the slower the reading speed), this effect being rather small in amplitude, but interestingly reversed compared to normal vision; (3) word length has no significant effect on reading time. Supporting the development of new and more effective assistive technology to help low vision is an important and timely issue, with massive potential implications for social and rehabilitation practices. The end goal of this project will be to use our findings to custom text simplification to this specific population and use it as an optimal and efficient reading aid.

Traditional approaches to set goals in second language (L2) vocabulary acquisition relied either on word lists that were obtained from large L1 corpora or on collective knowledge and experience of L2 experts, teachers, and examiners. Both approaches are known to offer some advantages, but also to have some limitations. In this paper, we try to combine both sources of information, namely the official reference level description for French language and the FLElex lexical database. Our aim is to train a statistical model on the French RLD that would be able to turn the distributional information from FLElex into one of the six levels of the Common European Framework of Reference for languages (CEFR). We show that such approach yields a gain of 29% in accuracy compared to the method currently used in the CEFRLex project. Besides, our experiments also offer deeper insights into the advantages and shortcomings of the two traditional sources of information (frequency vs. expert knowledge).