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| dialog | |
| 给定一问一答 post & response | |
| 似然为p(post,resp) | |
| p(post,resp) = p(post) x p(resp|post) | |
| 训练: loss function = -logp(resp|post) [交叉熵 伯努利分布] | |
| 测试:使p(resp|post)最大的resp [post已经给定] | |
| dialog & vae | |
| 由vae得知,一个样本点的似然: | |
| ##log p(post,resp)>= L = -KL (q(z|post,resp)||p(z)) + E_{z~q} log p(post,resp|z) | |
| log p(resp | post)>= L = -KL (q(z|resp,post)||p(z|post)) + E_{z~q} log p(resp|z, post) |
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