Predicting which words are considered hard to understand for a given target population is a vital step in many NLP applications such astext simplification. This task is commonly referred to as Complex Word Identification (CWI). With a few exceptions, previous studieshave approached the task as a binary classification task in which systems predict a complexity value (complex vs. non-complex) fora set of target words in a text. This choice is motivated by the fact that all CWI datasets compiled so far have been annotated using abinary annotation scheme. Our paper addresses this limitation by presenting the first English dataset for continuous lexical complexityprediction. We use a 5-point Likert scale scheme to annotate complex words in texts from three sources/domains: the Bible, Europarl,and biomedical texts. This resulted in a corpus of 9,476 sentences each annotated by around 7 annotators.

This work presents a replication study of Exploring Neural Text Simplification Models (Nisioi et al., 2017). We were able to successfully replicate and extend the methods presented in the original paper. Alongside the replication results, we present our improvements dubbed CombiNMT. By using an updated implementation of OpenNMT, and incorporating the Newsela corpus alongside the original Wikipedia dataset (Hwang et al., 2016), as well as refining both datasets to select high quality training examples. Our work present two new systems, CombiNMT995, which is a result of matched sentences with a cosine similarity of 0.995 or less, and CombiNMT98, which, similarly, runs on a cosine similarity of 0.98 or less. By extending the human evaluation presented within the original paper, increasing both the number of annotators and the number of sentences annotated, with the intention of increasing the quality of the results, CombiNMT998 shows significant improvement over any of the Neural Text Simplification (NTS) systems from the original paper in terms of both the number of changes and the percentage of correct changes made.