CoCo: A tool for automatically assessing conceptual complexity of texts

Traditional text complexity assessment usually takes into account only syntactic and lexical text complexity. The task of automatic assessment of conceptual text complexity, important for maintaining reader's interest and text adaptation for struggling readers, has only been proposed recently. In this paper, we present CoCo - a tool for automatic assessment of conceptual text complexity, based on using the current state-of-the-art unsupervised approach. We make the code and API freely available for research purposes, and describe the code and the possibility for its personalization and adaptation in details. We compare the current implementation with the state of the art, discussing the influence of the choice of entity linker on the performances of the tool. Finally, we present results obtained on two widely used text simplification corpora, discussing the full potential of the tool

Controllable Text Simplification with Explicit Paraphrasing

Text Simplification improves the readability of sentences through several rewriting transformations, such as lexical paraphrasing, deletion, and splitting. Current simplification systems are predominantly sequence-to-sequence models that are trained end-to-end to perform all these operations simultaneously. However, such systems limit themselves to mostly deleting words and cannot easily adapt to the requirements of different target audiences. In this paper, we propose a novel hybrid approach that leverages linguistically-motivated rules for splitting and deletion, and couples them with a neural paraphrasing model to produce varied rewriting styles. We introduce a new data augmentation method to improve the paraphrasing capability of our model. Through automatic and manual evaluations, we show that our proposed model establishes a new state-of-the-art for the task, paraphrasing more often than the existing systems, and can control the degree of each simplification operation applied to the input texts

Reference-less Quality Estimation of Text Simplification Systems

International audienceThe evaluation of text simplification (TS) systems remains an open challenge. As the task has common points with machine translation (MT), TS is often evaluated using MT metrics such as BLEU. However, such metrics require high quality reference data, which is rarely available for TS. TS has the advantage over MT of being a monolingual task, which allows for direct comparisons to be made between the simplified text and its original version. In this paper, we compare multiple approaches to reference-less quality estimation of sentence-level text simplification systems, based on the dataset used for the QATS 2016 shared task. We distinguish three different dimensions: gram-maticality, meaning preservation and simplicity. We show that n-gram-based MT metrics such as BLEU and METEOR correlate the most with human judgment of grammaticality and meaning preservation, whereas simplicity is best evaluated by basic length-based metrics

A Deeper Exploration of the Standard PB-SMT Approach to Text Simplification and its Evaluation

Comunicació presentada a: the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing del 26 al 31 de juliol de 2015 a Beijing, Xina.In the last few years, there has been a growing number of studies addressing the Text Simplification (TS) task as a monolingual machine translation (MT) problem which translates from ‘original’ to ‘simple’ language. Motivated by those results, we investigate the influence of quality vs quantity of the training data on the effectiveness of such a MT approach to text simplification. We conduct 40 experiments on the aligned sentences from English Wikipedia and Simple English Wikipedia, controlling for: (1) the similarity between the original and simplified sentences in the training and development datasets, and (2) the sizes of those datasets. The results suggest that in the standard PB-SMT approach to text simplification the quality of the datasets has a greater impact on the system performance. Additionally, we point out several important differences between cross-lingual MT and monolingual MT used in text simplification, and show that BLEU is not a good measure of system performance in text simplification task.The research described in this paper was partially funded by the project SKATER-UPFTALN (TIN2012-38584-C06-03), Ministerio de Econom´ıa y Competitividad, Secretar´ıa de Estado de Investigaci´on, Desarrollo e Innovaci´on, Spain, and the project ABLE-TO-INCLUDE (CIP-ICTPSP- 2013-7/621055). Hannah B´echara is supported by the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007-2013/ under REA grant agreement no. 31747

A Deeper exploration of the standard PB-SMT approach to text simplification and its evaluation

Comunicació presentada a: the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing del 26 al 31 de juliol de 2015 a Beijing, Xina.In the last few years, there has been a growing number of studies addressing the Text Simplification (TS) task as a monolingual machine translation (MT) problem which translates from ‘original’ to ‘simple’ language. Motivated by those results, we investigate the influence of quality vs quantity of the training data on the effectiveness of such a MT approach to text simplification. We conduct 40 experiments on the aligned sentences from English Wikipedia and Simple English Wikipedia, controlling for: (1) the similarity between the original and simplified sentences in the training and development datasets, and (2) the sizes of those datasets. The results suggest that in the standard PB-SMT approach to text simplification the quality of the datasets has a greater impact on the system performance. Additionally, we point out several important differences between cross-lingual MT and monolingual MT used in text simplification, and show that BLEU is not a good measure of system performance in text simplification task.The research described in this paper was partially funded by the project SKATER-UPFTALN (TIN2012-38584-C06-03), Ministerio de Econom´ıa y Competitividad, Secretar´ıa de Estado de Investigaci´on, Desarrollo e Innovaci´on, Spain, and the project ABLE-TO-INCLUDE (CIP-ICTPSP- 2013-7/621055). Hannah B´echara is supported by the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007-2013/ under REA grant agreement no. 31747