Simplifying metaphorical language for young readers: A corpus study on news text

The paper presents first results of an ongoing project on text simplification focusing on linguistic metaphors. Based on an analysis of a parallel corpus of news text professionally simplified for different grade levels, we identify six types of simplification choices falling into two broad categories: preserving metaphors or dropping them. An annotation study on almost 300 source sentences with metaphors (grade level 12) and their simplified counterparts (grade 4) is conducted. The results show that most metaphors are preserved and when they are dropped, the semantic content tends to be preserved rather than dropped, however, it is reworded without metaphorical language. In general, some of the expected tendencies in complexity reduction, measured with psycholinguistic variables linked to metaphor comprehension, are observed, suggesting good prospect for machine learning-based metaphor simplification. 1 Motivation and problem statement Text simplification is the process of meaning preserving reduction of discourse complexity whose purpose is to adapt text for specific populations of readers, for instance, children or language learners. The idea has been around since “My Weekly Reader” in the 1920s and Palmer’s work (1932) and over the past 20 years has attracted attention of the computational linguistics community. While broadly interpreted “lexical simplification” – in general understood as substitution of “difficult” words with “simpler” ones – is a common component of automated simplification systems (see, for instance, (Siddharthan, 2014)), studies of text simplification dedicated to specific lexis-related semantic phenomena are lacking. One class of such understudied phenomena are those related to figurative language; a surprising gap in the simplification research considering that metaphors have been shown to cause difficulties in text comprehension and that developing metaphor interpretation competence is a complex developmental process (for an overview, see, for instance, (Winner, 1997)). Since automated systems are trained on corpora of simplified text, understanding patterns of metaphor simplification based on corpus data could help improve simplification models. In this paper we present a study that is our first step in this direction. We analyze linguistic metaphors in a corpus of news texts professionally simplified for different grade levels. While editors’ guidelines instructed to avoid vivid metaphors, such as “paint into a corner”, our goal was to find out whether, and if so, how, linguistic metaphors in general are simplified by professional editors. Since ultimately we want to build automated metaphor simplification models, the purpose of this study is to investigate whether metaphors in a corpus of professionally simplified text, that is, potential training data, are simplified in systematic ways. Specifically, we were interested in two questions: 1) What types of discourse modifications do editors perform when simplifying metaphorical language? (in other words, whether a well-defined set of classes for the metaphor simplification task can be specified). 2) Do professional editors simplify metaphor phenomena in systematic ways? (if not, training simplification models using machine learning based on corpus data may not be promising). The paper’s structure follows the data-driven methodology adopted for this study: We first define the criteria used to identify the phenomenon in question: linguistic metaphor. Next, we present