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Therefore, we have attempted to facilitate the training of simplification rewritings using pre-training from a large-scale monolingual corpus such as Wikipedia articles. In addition, we propose a translation language model to seamlessly conduct a fine-tuning of text simplification from the pre-training of the language model. The experimental results show that the translation language model substantially outperforms a state-of-the-art model under a low-resource setting. In addition, a pre-trained translation language model with only 3000 supervised examples can achieve a performance comparable to that of the state-of-the-art model using 30,000 supervised examples. <\/jats:p>","DOI":"10.1142\/s2717554520500010","type":"journal-article","created":{"date-parts":[[2020,6,4]],"date-time":"2020-06-04T07:18:10Z","timestamp":1591255090000},"page":"2050001","source":"Crossref","is-referenced-by-count":3,"title":["Extremely Low-Resource Text Simplification with Pre-trained Transformer Language Model"],"prefix":"10.1142","volume":"30","author":[{"given":"Takumi","family":"Maruyama","sequence":"first","affiliation":[{"name":"Department of Electrical, Electronics and Information Engineering, Nagaoka University of Technology, 1603-1, Kamitomioka Nagaoka, Niigata 940-2188, Japan"}]},{"given":"Kazuhide","family":"Yamamoto","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronics and Information Engineering, Nagaoka University of Technology, 1603-1, Kamitomioka Nagaoka, Niigata 940-2188, Japan"}]}],"member":"219","published-online":{"date-parts":[[2020,5,30]]},"reference":[{"key":"S2717554520500010BIB001","first-page":"81","volume":"13","author":"Iwata K.","year":"2010","journal-title":"Jpn. 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