Semantic Features Based on Word Alignments for Estimating Quality of Text Simplification

2017-11-01 · IJCNLP 2017 11 · Tomoyuki Kajiwara, Atsushi Fujita

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.

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Tasks

Machine Translation Reading Comprehension Semantic Textual Similarity Text Generation Text SimplificationWord Embeddings

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