Abstract
Monolingual parallel corpus is crucial for training and evaluating text rewriting or paraphrasing models. Aligning parallel sentences between two large body of texts is a key step toward automatic construction of such parallel corpora. We propose a greedy alignment algorithm that makes use of strong unsupervised similarity measures. The algorithm aligns sentences with state-of-the-art accuracy while being more robust on corpora with special linguistic features. Using this alignment algorithm, we automatically constructed a large English parallel corpus from various translated works of classic literature.
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Notes
- 1.
The model names are abbreviated as “model + filter” schemes. For instance, “BLEU + UNV” means BLEU model for the first three stages of alignment, and Universal Sentence Encoder model for the last stage of filtering.
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Chen, X., Zhang, M., Zhu, K.Q. (2020). Aligning Sentences Between Comparable Texts of Different Styles. In: Wang, X., Lisi, F., Xiao, G., Botoeva, E. (eds) Semantic Technology. JIST 2019. Communications in Computer and Information Science, vol 1157. Springer, Singapore. https://doi.org/10.1007/978-981-15-3412-6_6
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DOI: https://doi.org/10.1007/978-981-15-3412-6_6
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