Deep Learning for Web Search and Natural Language Processing

In this talk, we first survey the latest deep learning technology, presenting both theoretical and practical perspectives that are most relevant to our topic. Next, we review general problems and tasks in text/language processing, and underline the distinct properties that differentiate language processing from other tasks such as speech and image object recognition. More importantly, we highlight the general issues of language processing, and elaborate on how new deep learning technologies are proposed and fundamentally address these issues. We then place particular emphasis on several important applications: 1) web search, 2) online recommendation and 3) machine translation. For each of the tasks we discuss what particular architectures of deep learning models are suitable given the nature of the task, and how learning can be performed efficiently and effectively using end-to-end optimization strategies. Beyond providing a systematic review of the general theory, we also present hands-on experience in building state-of-the-art systems. In the talk, we will share our practice with concrete examples drawn from our first-hand experience in major research benchmarks and some industrial scale applications which we have been working on extensively in recent years.