Abstract
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 identification (CWI) model that exploits both lexical and contextual features, and a simplification mechanism which relies on a word-embedding lexical substitution model to replace the detected complex words with simpler paraphrases. We compare our CWI and lexical simplification models to several baselines, and evaluate the performance of our simplification system against human judgments. The results show that our models are able to detect complex words with higher accuracy than other commonly used methods, and propose good simplification substitutes in context. They also highlight the limited contribution of context features for CWI, which nonetheless improve simplification compared to context-unaware models.
Highlights
Automated text simplification is the process that involves transforming a complex text into one with the same meaning, but can be more read and understood by a broader audience (Saggion, 2017)
We focus on lexical simplification, the task of replacing difficult words in a text with words that are easier to understand
We extend this work and adapt the Melamud et al (2015) model to the simplification setting by using candidate paraphrases extracted from the Simple Paraphrase Database (PPDB) resource (Pavlick and Callison-Burch, 2016), a subset of the PPDB that contains complex words and phrases, and their simpler counterparts that can be used for incontext simplification
Summary
Automated text simplification is the process that involves transforming a complex text into one with the same meaning, but can be more read and understood by a broader audience (Saggion, 2017). This process includes several subtasks such as complex word and sentence identification, lexical simplification, syntactic simplification, and sentence splitting. Lexical simplification involves two main processes: identifying complex words within a text, and suggesting simpler paraphrases for these words that preserve their meaning in this context. We show that our complex word identification classifier and substitution model improve over several baselines which exploit other types of information and do not account for context. Our approach proposes highly accurate substitutes that are simpler than the target words and preserve the meaning of the corresponding sentences
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