A Keyword-based Monolingual Sentence Aligner in Text Simplification

We introduce a method for learning to align sentences in monolingual parallel articles for text simplification. In our approach, word keyness is integrated to prefer aligning essential words in sentences. The method involves estimating word keyness based on TF*IDF and semantic PageRank, and word nodes’ parts-of-speech and degrees of reference. At run-time, the keyword analyses are used as word weights in sentence similarity measure. And a global dynamic programming goes through sentence similarities further weighted by aligned content-word ratios and positions of aligned words to determine the optimal candidates of parallel sentences. We present a prototype sentence aligner, KEA, that applies the method to monolingual parallel articles. Evaluation shows that KEA pays more attention to key words during sentence aligning and outperforms the current state-of-the-art in alignment accuracy and f-measure. Our pilot study also indicates that language learners benefit from our sentence-aligned parallel articles in reading comprehension test.