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 …
In order to simplify a sentence, human editors perform multiple rewriting transformations: they split it into several shorter sentences, paraphrase words (i.e. replacing complex words or phrases by simpler synonyms), reorder components, and/or delete …
We introduce EASSE, a Python package aiming to facilitate and standardise automatic evaluation and comparison of Sentence Simplification (SS) systems. EASSE provides a single access point to a broad range of evaluation resources: standard automatic …
Current approaches to Text Simplification focus on simplifying sentences individually. However, certain simplification transformations span beyond single sentences (e.g. joining and re-ordering sentences). In this paper, we motivate the need for …
Complex Word Identification (CWI) is the task of identifying which words or phrases in a sentence are difficult to understand by a target audience. The latest CWI Shared Task released data for two settings: monolingual (i.e. train and test in the …
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 operations, such as deletions …
We introduce MASSAlign: a Python library for the alignment and annotation of monolingual comparable documents. MASSAlign offers easy-to-use access to state of the art algorithms for paragraph and sentence-level alignment, as well as novel algorithms …
Text Complexity Analysis is an useful task in Education. For example, it can help teachers select appropriate texts for their students according to their educational level. This task requires the analysis of several text features that people do …
One of the main research challenges in Semantic Role Labeling (SRL) is the development of systems for languages other than English. For Brazilian Portuguese, a corpus with appropriate manually-annotated data, PropBank.Br, has recently become …
One of the main research challenges in semantic role labeling (SRL) is the development of applications for languages other than English. For Brazilian Portuguese, recent projects in lexical semantics are about to provide the necessary computational …