Semantic Features Based on Word Alignments for Estimating Quality of Text Simplification
Tomoyuki Kajiwara, Atsushi Fujita · International Joint Conference on Natural Language Processing · 2017
This paper examines the usefulness of semantic features based on word alignments for estimating the quality of text simplification. Specifically, we introduce seven types of alignment-based features computed on the basis of word embeddings and paraphrase lexicons. Through an empirical experiment using the QATS dataset, we confirm that we can achieve the state-of-the-art performance only with these features.