{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T05:08:43Z","timestamp":1790485723649,"version":"4.1.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>This paper focuses on the task of sentiment transfer on non-parallel text, which modifies sentiment attributes (e.g., positive or negative) of sentences while preserving their attribute-independent contents. Existing methods adopt RNN encoder-decoder structure to generate a new sentence of a target sentiment word by word, which is trained on a particular dataset from scratch and have limited ability to produce satisfactory sentences. When people convert the sentiment attribute of a given sentence, a simple but effective approach is to only replace the sentiment tokens of the sentence with other expressions indicative of the target sentiment, instead of building a new sentence from scratch. Such a process is very similar to the task of Text Infilling or Cloze. With this intuition, we propose a two steps approach: Mask and Infill. In the \\emph{mask} step, we identify and mask the sentiment tokens of a given sentence. In the \\emph{infill} step, we utilize a pre-trained Masked Language Model (MLM) to infill the masked positions by predicting words or phrases conditioned on the context\\footnote{In this paper, \\emph{content} and \\emph{context} are equivalent, \\emph{style}, \\emph{attribute} and \\emph{label} are equivalent.}and target sentiment. We evaluate our model on two review datasets \\emph{Yelp} and \\emph{Amazon} by quantitative, qualitative, and human evaluations. Experimental results demonstrate that our model achieve state-of-the-art performance on both accuracy and BLEU scores.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/732","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:46:05Z","timestamp":1564285565000},"page":"5271-5277","source":"Crossref","is-referenced-by-count":39,"title":["Mask and Infill: Applying Masked Language Model for Sentiment Transfer"],"prefix":"10.24963","author":[{"given":"Xing","family":"Wu","sequence":"first","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China"},{"name":"University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China"},{"name":"University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liangjun","family":"Zang","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jizhong","family":"Han","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Songlin","family":"Hu","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","theme":"Artificial Intelligence","location":"Macao, China","acronym":"IJCAI-2019","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2019,8,10]]},"end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:51:26Z","timestamp":1564285886000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/732"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/732","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}