Neural text normalization leveraging similarities of strings and sounds
Tokyo Institute of Technology（东京工业大学）；National Institute of Advanced Industrial Science and Technology（国立先进工业科学技术研究所）
英文摘要：
We propose neural models that can normalize text by considering the similarities of word strings and sounds. We experimentally compared a model that considers the similarities of both word strings and sounds, a model that considers only the similarity of word strings or of sounds, and a model without the similarities as a baseline. Results showed that leveraging the word string similarity succeeded in dealing with misspellings and abbreviations, and taking into account the sound similarity succeeded in dealing with phonetic substitutions and emphasized characters. So that the proposed models achieved higher F$_1$ scores than the baseline.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
