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• W3035008906 abstract "The success of a text simplification system heavily depends on the quality and quantity of complex-simple sentence pairs in the training corpus, which are extracted by aligning sentences between parallel articles. To evaluate and improve sentence alignment quality, we create two manually annotated sentence-aligned datasets from two commonly used text simplification corpora, Newsela and Wikipedia. We propose a novel neural CRF alignment model which not only leverages the sequential nature of sentences in parallel documents but also utilizes a neural sentence pair model to capture semantic similarity. Experiments demonstrate that our proposed approach outperforms all the previous work on monolingual sentence alignment task by more than 5 points in F1. We apply our CRF aligner to construct two new text simplification datasets, Newsela-Auto and Wiki-Auto, which are much larger and of better quality compared to the existing datasets. A Transformer-based seq2seq model trained on our datasets establishes a new state-of-the-art for text simplification in both automatic and human evaluation." @default.
• W3035008906 created "2020-06-19" @default.
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• W3035008906 date "2020-01-01" @default.
• W3035008906 modified "2023-09-30" @default.
• W3035008906 title "Neural CRF Model for Sentence Alignment in Text Simplification" @default.
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• W3035008906 doi "https://doi.org/10.18653/v1/2020.acl-main.709" @default.
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