same

build/html/_sources/introduction/Abstract.md.txt

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+# 摘要
+
+教机器理解人类语言文档是人工智能领域最难以捉摸和长期存在的挑战之一。本文探讨了阅读理解的问题:如何构建计算机系统来阅读一篇文章并回答理解问题。一方面，我们认为阅读理解是评估计算机系统理解人类语言能力的一项重要任务。另一方面，如果我们能够构建高性能的阅读理解系统，它们将成为问答和对话系统等应用的关键技术。
+本文主要研究了基于深度神经网络的阅读理解模型。与传统的稀疏的、手工设计的基于特征的模型相比，这些端到端神经模型被证明在学习丰富的语言现象方面更有效，并在很大程度上提高了所有现代阅读理解基准的性能。
+本文由两部分组成。第一部分，我们的目标是涵盖神经阅读理解的本质，并介绍我们在构建有效的神经阅读压缩模型方面所做的努力，更重要的是了解神经阅读理解模型实际学习了什么，以及解决当前任务需要多大的语言理解深度。我们还总结了该领域的最新进展，并讨论了该领域未来的发展方向和有待解决的问题。
+在本文的第二部分，我们探讨了如何基于最近神经阅读理解的成功构建实际应用。特别是，我们开创了两个新的研究方向:1)如何将信息检索技术与神经阅读理解相结合，解决大规模开放领域的问题回答;和2)如何从现有的单轮、基于斯潘语言的阅读理解模型中构建会话问答系统。我们在DRQA和COQA项目中实现了这些想法，并证明了这些方法的有效性。我们相信它们对未来的语言技术有着巨大的前景。
+
+

build/html/_sources/introduction/Acknowledge.md.txt

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+# Acknowledgements
+
+The past six years at Stanford have been an unforgettable and invaluable experience to me. When I first started my PhD in 2012, I could barely speak fluent English (I was required to take five English courses at Stanford), knew little about this country and had never heard of the term “natural language processing”. It is unbelievable that over the following years I have actually been doing research about language and training computer systems to understand human languages (English in most cases), as well as training myself to speak and write in English. At the same time, 2012 is the year that deep neural networks (also called deep learning) started to take off and dominate almost all the AI applications we are seeing today. I witnessed how fast Artificial Intelligence has been developing from the beginning of the journey and feel quite excited —– and occasionally panicked —– to be a part of this trend. I would not have been able to make this journey without the help and support of many, many people and I feel deeply indebted to them. 
+First and foremost, my greatest thanks go to my advisor Christopher Manning. I really didn’t know Chris when I first came to Stanford — only after a couple of years that I worked with him and learned about NLP, did I realize how privileged I am to get to work with one of the most brilliant minds in our field. He always has a very insightful, high- level view about the field while he is also uncommonly detail oriented and understands the nature of the problems very well. More importantly, Chris is an extremely kind, caring and supportive advisor that I could not have asked for more. He is like an older friend of mine (if he doesn’t mind me saying so) and I can talk with him about everything. He always believes in me even though I am not always that confident about myself. I am forever grateful to him and I have already started to miss him. 
+I would like to thank Dan Jurafsky and Percy Liang — the other two giants of the Stanford NLP group — for being on my thesis committee and for a lot of guidance and help throughout my PhD studies. Dan is an extremely charming, enthusiastic and knowl- edgeable person and I always feel my passion getting ignited after talking to him. Percy is a superman and a role model for all the NLP PhD students (at least myself). I never understand how one can accomplish so many things at the same time and a big part of this dissertation is built on top of his research. I want to thank Chris, Dan and Percy, for setting up the Stanford NLP Group, my home at Stanford, and I will always be proud to be a part of this family. 
+It is also my great honor to have Luke Zettlemoyer on my thesis committee. The work presented in this dissertation is very relevant to his research and I learned a lot from his papers. I look forward to working with him in the near future. I also would like to thank Yinyu Ye for his time chairing my thesis defense. 
+During my PhD, I have done two wonderful internships at Microsoft Research and Facebook AI Research. I thank my mentors Kristina Toutanova, Antoine Bordes and Jason Weston when I worked at these places. My internship project at Facebook eventually leads to the DRQA project and a part of this dissertation. I also would like to thank Microsoft and Facebook for providing me with fellowships. 
+Collaboration is a big lesson that I learned, and also a fun part of graduate school. I thank my fellow collaborators: Gabor Angeli, Jason Bolton, Arun Chaganty, Adam Fisch, Jon Gauthier, Shayne Longpre, Jesse Mu, Siva Reddy, Richard Socher, Yuhao Zhang, Vic- tor Zhong, and others. In particular, Richard — with him I finished my first paper in graduate school. He had very clear sense about how to define an impactful research project while I had little experience at the time. Adam and Siva — with them I finished the DRQA and COQA projects respectively. Not only am I proud of these two projects, but also I greatly enjoyed the collaborations. We have become good friends afterwards. The KBP team, especially Yuhao, Gabor and Arun — I enjoyed the teamwork during those two sum- mers. Jon, Victor, Shayne and Jesse, the younger people that I got to work with, although I wish I could have done a better job. I also want to thank the two teaching teams (7 and 25 people respectively) for the NLP class that I’ve worked on and that was a very unique and rewarding experience for me. 
+I thank the whole Stanford NLP Group, especially Sida Wang, Will Monroe, Angel Chang, Gabor Angeli, Siva Reddy, Arun Chaganty, Yuhao Zhang, Peng Qi, Jacob Stein- hardt, Jiwei Li, He He, Robin Jia and Ziang Xie, who gave me a lot of support at various times. I am even not sure if there could be another research group in the world better than our group (I hope I can create a similar one in the future). The NLP retreat, NLP BBQ and those paper swap nights were among my most vivid memories in graduate school. 
+Outside of the NLP group, I have been extremely lucky to be surrounded by many great friends. Just to name a few (and forgive me for not being able to list all of them): Yanting Zhao, my close friend for many years, who keeps pulling me out from my stressful PhD life, and I share a lot of joyous moments with her. Xueqing Liu, my classmate and roommate in college who started her PhD at UIUC in the same year and she is the person that I can keep talking to and exchanging my feelings and thoughts with, especially on those bad days. Tao Lei, a brilliant NLP PhD and my algorithms “teacher” in high school and I keep learning from him and getting inspired from every discussion. Thanh-Vy Hua, my mentor and “elder sister” who always makes sure that I am still on the right track of my life and taught me many meta-skills to survive this journey (even though we have only met 3 times in the real world). Everyone in the “caˇo yu ́” group, I am so happy that I have spent many Friday evenings with you. 
+During the past year, I visited a great number of U.S. universities seeking an academic job position. There are so many people I want to thank for assistance along the way —– I either received great help and advice from them, or I felt extremely welcomed during my visit —– including Sanjeev Arora, Yoav Artzi, Regina Barzilay, Chris Callison-Burch, Kai-Wei Chang, Kyunghyun Cho, William Cohen, Michael Collins, Chris Dyer, Jacob Eisenstein, Julia Hirschberg, Julia Hockenmaier, Tengyu Ma, Andrew McCallum, Kathy McKeown, Rada Mihalcea, Tom Mitchell, Ray Mooney, Karthik Narasimhan, Graham Neubig, Christos Papadimitriou, Nanyun Peng, Drago Radev, Sasha Rush, Fei Sha, Yulia Tsvetkov, Luke Zettlemoyer and many others. These people are really a big part of the reasons that I love our research community so much, therefore I want to follow their paths and dedicate myself to an academic career. I hope to continue to contribute to our research community in the future. 
+A special thanks to Andrew Chi-Chih Yao for creating the Special Pilot CS Class where I did my undergraduate studies. I am super proud of being a part of the “Yao class” family. I also thank Weizhu Chen, Qiang Yang and Haixun Wang, with them I received my very first research experience. With their support, I was very fortunate to have the opportunity to come to Stanford for my PhD. 
+I thank my parents: Zhi Chen and Hongmei Wang. Like most Chinese students in my generation, I am the only child of my family and I have a very close relationship with them — even if they are living 16 (or 15) hours ahead of me and I can only spare 2–3 weeks staying with them every year. My parents made me who I am today and I never know how to pay them back. I hope that they are at least a little proud of me for what I have been through so far. 
+Lastly, I would like to thank Huacheng for his love and support (we got married 4 months before this dissertation was submitted). I was fifteen when I first met Huacheng and we have been experiencing almost everything together since then: from high-school programming competitions, to our wonderful college time at Tsinghua University and we both made it to the Stanford CS PhD program in 2012. For over ten years in the past, he is not only my partner, my classmate, my best friend, but also the person I admire most, for his modesty, intelligence, concentration and hard work. Without him, I would not have come to Stanford. Without him, I would also not have taken the job at Princeton. I thank him for everything he has done for me. 
+
+
+To my parents and Huacheng, for their unconditional love. 
+

build/html/_sources/introduction/Preface.md.txt

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+# Preface 译者 注
+
+我一直觉得阅读一本优秀的博士毕业论文是最快了解一个领域的方式。论文中会介绍它的研究的前因后果以及最近和未来的发展趋势，并且，这里面会引用大量的参考文献，这都是宝贵的经过整理的学习资料。
+其次，我们可以从行文思路中学习到作者的思维方式，从其发表文章的先后顺序和年代，看到作者的成长。我没有翻译作者的Acknowledgment，但是我强烈建议大家去读一下，了解一下作者的心态。
+同时我强烈推荐phD Grind这篇文章，也是讲述了斯坦福大学的博士生的学习心路历程。推荐理由引用其中的一段话：有些人可以通过博士的学习生涯学习到一种思维方式，有的人没有通过博士的学习生涯，但是通过别的方式同样达到了这样的高度。
+NLP领域逐渐进入人们的视野，从word2vec到BERT再到后来提出的一些模型，都是在dig预训练这个环节。
+才疏学浅，有不足之处，还望指出。谢谢。
+本文翻译已经咨询过原作者。
+本文仅供学习交流所用，一切权利由原作者及单位保留，译者不承担法律责任。
+
+
+叶兀
+2019年
+

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+<h1>摘要<a class="headerlink" href="#id1" title="永久链接至标题">¶</a></h1>
+<p>教机器理解人类语言文档是人工智能领域最难以捉摸和长期存在的挑战之一。本文探讨了阅读理解的问题:如何构建计算机系统来阅读一篇文章并回答理解问题。一方面，我们认为阅读理解是评估计算机系统理解人类语言能力的一项重要任务。另一方面，如果我们能够构建高性能的阅读理解系统，它们将成为问答和对话系统等应用的关键技术。
+本文主要研究了基于深度神经网络的阅读理解模型。与传统的稀疏的、手工设计的基于特征的模型相比，这些端到端神经模型被证明在学习丰富的语言现象方面更有效，并在很大程度上提高了所有现代阅读理解基准的性能。
+本文由两部分组成。第一部分，我们的目标是涵盖神经阅读理解的本质，并介绍我们在构建有效的神经阅读压缩模型方面所做的努力，更重要的是了解神经阅读理解模型实际学习了什么，以及解决当前任务需要多大的语言理解深度。我们还总结了该领域的最新进展，并讨论了该领域未来的发展方向和有待解决的问题。
+在本文的第二部分，我们探讨了如何基于最近神经阅读理解的成功构建实际应用。特别是，我们开创了两个新的研究方向:1)如何将信息检索技术与神经阅读理解相结合，解决大规模开放领域的问题回答;和2)如何从现有的单轮、基于斯潘语言的阅读理解模型中构建会话问答系统。我们在DRQA和COQA项目中实现了这些想法，并证明了这些方法的有效性。我们相信它们对未来的语言技术有着巨大的前景。</p>
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