update

Chapter16/chapter16.tex

@@ -296,7 +296,11 @@
 %----------------------------------------------------------------------------------------
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 \subsection{对偶学习}
 \subsection{对偶学习}
 
 
-\parinterval 对称，也许是人类最喜欢的美，其始终贯穿在整个人类文明的诞生与发展之中。古语“夫美者，上下、内外、大小、远近皆无害焉，故曰美”描述的即是这样的美。在人工智能的任务中，也存在着这样的对称结构，比如机器翻译中英译汉和汉译英、图像处理中的图像标注和图像生成以及语音处理中的语音识别和语音合成等。利用这些任务的对称性质（也称对偶性），可以使互为对偶的两个任务获得更有效的反馈，从而使对应的模型相互学习、相互提高。目前，对偶学习的思想已经广泛应用于自然语言处理、图像处理等领域，它不仅能够提升在有限双语资源下的翻译模型性能（{\small\bfnew{有监督对偶学习}}，Dual Supervised Learning\index{Dual Supervised Learning}）\upcite{DBLP:conf/icml/XiaQCBYL17,DBLP:conf/acl/SuHC19,DBLP:journals/ejasmp/RadzikowskiNWY19}，而且能够利用未标注的单语数据来进行学习（{\small\bfnew{无监督对偶学习}}，Dual Unsupervised Learning\index{Dual Unsupervised Learning}）\upcite{qin2020dual,DBLP:conf/iccv/YiZTG17,DBLP:journals/access/DuRZH20}。下面将一一展开讨论。
+\parinterval 对称，也许是人类最喜欢的美，其始终贯穿在整个人类文明的诞生与发展之中。古语“夫美者，上下、内外、大小、远近皆无害焉，故曰美”描述的即是这样的美。在人工智能的任务中，也存在着这样的对称结构，比如机器翻译中英译汉和汉译英、图像处理中的图像标注和图像生成以及语音处理中的语音识别和语音合成等。利用这些任务的对称性质（也称对偶性），可以使互为对偶的两个任务获得更有效的反馈，从而使对应的模型相互学习、相互提高。
+
+
+
+目前，对偶学习的思想已经广泛应用于低资源机器翻译领域，它不仅能够提升在有限双语资源下的翻译模型性能（{\small\bfnew{有监督对偶学习}}，Dual Supervised Learning\index{Dual Supervised Learning}）\upcite{DBLP:conf/icml/XiaQCBYL17,DBLP:conf/icml/XiaTTQYL18}，而且能够利用未标注的单语数据来进行学习（{\small\bfnew{无监督对偶学习}}，Dual Unsupervised Learning\index{Dual Unsupervised Learning}）\upcite{qin2020dual,DBLP:conf/nips/HeXQWYLM16,zhao2020dual}。下面将一一展开讨论。
 
 
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bibliography.bib

@@ -6329,7 +6329,7 @@ author    = {Yoshua Bengio and
 }
 }
 
 
 @inproceedings{DBLP:conf/wmt/DumaM17,
 @inproceedings{DBLP:conf/wmt/DumaM17,
-  author    = {Mirela{-}Stefania Duma and
+  author    = {Mirela-Stefania Duma and
                Wolfgang Menzel},
                Wolfgang Menzel},
   title     = {Automatic Threshold Detection for Data Selection in Machine Translation},
   title     = {Automatic Threshold Detection for Data Selection in Machine Translation},
   pages     = {483--488},
   pages     = {483--488},
@@ -6602,7 +6602,7 @@ author    = {Yoshua Bengio and
 }
 }
 
 
 @article{DBLP:journals/corr/ZhuB17,
 @article{DBLP:journals/corr/ZhuB17,
-  author    = {Jia{-}Jie Zhu and
+  author    = {Jia-Jie Zhu and
                Jos{\'{e}} Bento},
                Jos{\'{e}} Bento},
   title     = {Generative Adversarial Active Learning},
   title     = {Generative Adversarial Active Learning},
   journal   = {CoRR},
   journal   = {CoRR},
@@ -6956,8 +6956,8 @@ author    = {Yoshua Bengio and
 }
 }
 
 
 @inproceedings{DBLP:conf/nips/ChangLM17,
 @inproceedings{DBLP:conf/nips/ChangLM17,
-  author    = {Haw{-}Shiuan Chang and
-               Erik G. Learned{-}Miller and
+  author    = {Haw-Shiuan Chang and
+               Erik G. Learned-Miller and
                Andrew McCallum},
                Andrew McCallum},
   title     = {Active Bias: Training More Accurate Neural Networks by Emphasizing
   title     = {Active Bias: Training More Accurate Neural Networks by Emphasizing
                High Variance Samples},
                High Variance Samples},
@@ -13097,6 +13097,42 @@ author    = {Zhuang Liu and
   volume    = {abs/1910.01108},
   volume    = {abs/1910.01108},
   year      = {2019}
   year      = {2019}
 }
 }
+@inproceedings{DBLP:conf/icml/XiaTTQYL18,
+  author    = {Yingce Xia and
+               Xu Tan and
+               Fei Tian and
+               Tao Qin and
+               Nenghai Yu and
+               Tie-Yan Liu},
+  title     = {Model-Level Dual Learning},
+  series    = {Proceedings of Machine Learning Research},
+  volume    = {80},
+  pages     = {5379--5388},
+  publisher = {International Conference on Machine Learning},
+  year      = {2018}
+}
+
+
+@inproceedings{DBLP:conf/nips/HeXQWYLM16,
+  author    = {Di He and
+               Yingce Xia and
+               Tao Qin and
+               Liwei Wang and
+               Nenghai Yu and
+               Tie{-}Yan Liu and
+               Wei{-}Ying Ma},
+  title     = {Dual Learning for Machine Translation},
+  publisher = {Conference and Workshop on Neural Information Processing Systems},
+  pages     = {820--828},
+  year      = {2016}
+}
+@article{zhao2020dual,
+  title={Dual Learning: Theoretical Study and an Algorithmic Extension},
+  author={Zhao, Zhibing and Xia, Yingce and Qin, Tao and Xia, Lirong and Liu, Tie-Yan},
+  journal={arXiv preprint arXiv:2005.08238},
+  year={2020}
+}
+
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