Everyone interested in this topic is welcomed to join us. the following is the abstract for this week\u2019s paper.<\/p>\n\n\n\n

\n\t\t

\n\t\t\tSentiGAN: Generating Sentimental Texts via Mixture Adversarial Networks\n\t\t<\/p>\n\t\t

\n\t\t\tKe Wang,\nXiaojunWan\n\t\t<\/p>\n\t\t

\n\t\t\tAbstract\n\t\t<\/p>\n\t\t

\n\t\t\t Generating texts of different sentiment\nlabels is getting more and more attention in the area of natural language\ngeneration. Recently, Generative Adversarial Net (GAN) has shown promising\nresults in text generation. However, the texts generated by GAN usually suffer\nfrom the problems of poor quality, lack of diversity and mode collapse. In this\npaper, we propose a novel framework – SentiGAN, which has multiple generators\nand one multi-class discriminator, to address the above problems. In our\nframework, multiple generators are trained simultaneously, aiming at generating\ntexts of different sentiment labels without supervision. We propose a penalty\nbased objective in the generators to force each of them to generate diversified\nexamples of a specific sentiment label. Moreover, the use of multiple\ngenerators and one multi-class discriminator can make each generator focus on\ngenerating its own examples of a specific sentiment label accurately.\nExperimental results on four datasets demonstrate that our model consistently\noutperforms several state-of-the-art text generation methods in the sentiment\naccuracy and quality of generated texts.<\/span>\n\t\t<\/p>\n\t\t

\n\t\t\t<\/span>\n\t\t<\/p>\n\t<\/div>\n<\/p>\n\n\n\n

SentiGAN: Generating Sentimental Texts via Mixture Adversarial Networks<\/a>\u4e0b\u8f7d<\/a><\/div>\n\n\n\n\n\n\n\n

NLPIR SEMINAR 20th ISSUE COMPLETED<\/strong><\/h2>\n\n\n\n
Last Monday, Ziyu Liu<\/strong> gave a presentation about the paper, SentiGAN: Generating Sentimental Texts via Mixture Adversarial Networks<\/strong>, and shared some opinion on it.<\/p>\n\n\n\n
$\"\"$ <\/figure><\/div>\n\n\n\n
The evaluation metrics to measure the quality of generated sentences contains four aspects \u2013 fluency, novelty, diversity and intelligibility. Through the case study, the sentences produced by SentiGAN are more readable and of higher quality.<\/p>\n","protected":false},"excerpt":{"rendered":"
NLPIR SEMINAR Y2019#7 INTRO In the new s … \u7ee7\u7eed\u9605\u8bfb →<\/span><\/a><\/p>\n","protected":false},"author":862,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[37,38],"tags":[],"_links":{"self":[{"href":"http:\/\/www.nlpir.org\/wordpress\/wp-json\/wp\/v2\/posts\/6767"}],"collection":[{"href":"http:\/\/www.nlpir.org\/wordpress\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.nlpir.org\/wordpress\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.nlpir.org\/wordpress\/wp-json\/wp\/v2\/users\/862"}],"replies":[{"embeddable":true,"href":"http:\/\/www.nlpir.org\/wordpress\/wp-json\/wp\/v2\/comments?post=6767"}],"version-history":[{"count":2,"href":"http:\/\/www.nlpir.org\/wordpress\/wp-json\/wp\/v2\/posts\/6767\/revisions"}],"predecessor-version":[{"id":6780,"href":"http:\/\/www.nlpir.org\/wordpress\/wp-json\/wp\/v2\/posts\/6767\/revisions\/6780"}],"wp:attachment":[{"href":"http:\/\/www.nlpir.org\/wordpress\/wp-json\/wp\/v2\/media?parent=6767"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.nlpir.org\/wordpress\/wp-json\/wp\/v2\/categories?post=6767"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.nlpir.org\/wordpress\/wp-json\/wp\/v2\/tags?post=6767"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}

INTRO <\/h3>\n\n\n\n In the new semester, our Lab, Web Search Mining and Security Lab, plans to hold an academic seminar every Monday, and each time a keynote speaker will share understanding of papers on his\/her related research with you.<\/p>\n\n\n\n

INTRO <\/h3>\n\n\n\n
In the new semester, our Lab, Web Search Mining and Security Lab, plans to hold an academic seminar every Monday, and each time a keynote speaker will share understanding of papers on his\/her related research with you.
<\/p>\n\n\n\n