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Data Science Archive@DataScienceArchive P.53
DATASCIENCEARCHIVE Telegram 53
NIPS 2018 creativity workshop 上一篇关于歌词生成的 paper。对于生成模型来说,特别是需要一些创造力的问题,传统的 NLU 的 metrics (比如翻译常用的 BLEU)不是很好用,得到分数很高的未必会让人感觉好。
先前自己做对联机的时候也有这样的感觉,有的模型虽然 BLEU,Perplexity 都很低,但反倒直观上看起来并不怎么样。
文章中对生成歌词这个问题同时分别在歌词和书籍语料上生成了两个language model,同时让歌词拥有歌词的特点(看起来是捕捉韵脚,对仗,重复加重情感),也具有书籍的特点(词汇量丰富,表达多样性)。
参考意义应该还是挺大,尤其是对于需要创造力的生成问题,利用多个不同的 multi language model 来进行 ensemble 的思想尤为值得借鉴。
这个 workshop 也是NIPS 中一直比较关注的,经常有很多很有意思的 paper。
作者来自Google Brain。
workshop homepage: https://nips2018creativity.github.io/
paper: https://arxiv.org/abs/1811.04651



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NIPS 2018 creativity workshop 上一篇关于歌词生成的 paper。对于生成模型来说,特别是需要一些创造力的问题,传统的 NLU 的 metrics (比如翻译常用的 BLEU)不是很好用,得到分数很高的未必会让人感觉好。
先前自己做对联机的时候也有这样的感觉,有的模型虽然 BLEU,Perplexity 都很低,但反倒直观上看起来并不怎么样。
文章中对生成歌词这个问题同时分别在歌词和书籍语料上生成了两个language model,同时让歌词拥有歌词的特点(看起来是捕捉韵脚,对仗,重复加重情感),也具有书籍的特点(词汇量丰富,表达多样性)。
参考意义应该还是挺大,尤其是对于需要创造力的生成问题,利用多个不同的 multi language model 来进行 ensemble 的思想尤为值得借鉴。
这个 workshop 也是NIPS 中一直比较关注的,经常有很多很有意思的 paper。
作者来自Google Brain。
workshop homepage: https://nips2018creativity.github.io/
paper: https://arxiv.org/abs/1811.04651

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