Neu-IR: The SIGIR 2016 Workshop on Neural Information Retrieval

Recurrent Networks and Beyond

Tomas Mikolov, Facebook AI Research

(opens in new tab)Abstract: In this talk, I will give a brief overview of recurrent networks and their applications. I will then present several extensions that aim to help these powerful models to learn more patterns from training data. This will include a simple modification of the architecture that allows to capture longer context information, and an architecture that allows to learn complex algorithmic patterns. The talk will be concluded with a discussion of a long term research plan on how to advance machine learning techniques towards development of artificial intelligence.

Bio: Tomas Mikolov (opens in new tab) is a research scientist at Facebook AI Research since May 2014. Previously he has been a member of the Google Brain team, where he developed and implemented efficient algorithms for computing distributed representations of words (word2vec project). He obtained his PhD from Brno University of Technology (Czech Republic) for his work on recurrent neural network based language models (RNNLM). His long term research goal is to develop intelligent machines capable of learning and communication with people using natural language.

Does IR Need Deep Learning?

Hang Li, Huawei Technologies

(opens in new tab)Abstract: In recent years, deep learning has become the key technology of state-of-the-art systems in many areas of computer science, such as computer vision, speech processing, and natural language processing. A question naturally arises, that is, can deep learning also bring breakthrough into IR (information retrieval)? In fact, there has been a large amount of effort made to address the question and significant progress has been achieved. Yet there is still doubt about whether it is the case.

In this talk, I will argue that, if we take a broad view on IR, then we arrive at a conclusion that deep learning can indeed greatly boost IR. Actually it has been observed that deep learning can make great improvements on some hard problems in IR such as question answering from knowledge base, image retrieval, etc; on the other hand, for some traditional IR tasks, in some sense easy tasks, such as document retrieval, the improvements might not be so notable. I will introduce some of the work on deep learning for IR conducted at Huawei Noah’s Ark Lab, to support my claim. I will also make discussions on the strength and limitation of deep learning, IR problems on which deep learning can potentially make significant contributions, as well as future directions of research on IR.

Bio: Hang Li (opens in new tab) is director of the Noah’s Ark Lab of Huawei Technologies, adjunct professors of Peking University and Nanjing University. He is ACM Distinguished Scientist. His research areas include information retrieval, natural language processing, statistical machine learning, and data mining. Hang graduated from Kyoto University in 1988 and earned his PhD from the University of Tokyo in 1998. He worked at the NEC lab as researcher during 1991 and 2001, and Microsoft Research Asia as senior researcher and research manager during 2001 and 2012. He joined Huawei Technologies in 2012. Hang has published three technical books, and more than 120 technical papers at top international conferences including SIGIR, WWW, WSDM, ACL, EMNLP, ICML, NIPS, SIGKDD, AAAI, IJCAI, and top international journals including CL, NLE, JMLR, TOIS, IRJ, IPM, TKDE, TWEB, TIST. He and his colleagues’ papers received the SIGKDD’08 best application paper award, the SIGIR’08 best student paper award, the ACL’12 best student paper award. Hang worked on the development of several products such as Microsoft SQL Server 2005, Office 2007, Live Search 2008, Bing 2009, Office 2010, Bing 2010, Office 2012, Huawei Smartphones 2014. He has 42 granted US patents. Hang is also very active in the research communities and has served or is serving top international conferences as PC chair, Senior PC member, or PC member, including SIGIR, WWW, WSDM, ACL, NACL, EMNLP, NIPS, SIGKDD, ICDM, IJCAI, ACML, and top international journals as associate editor or editorial board member, including CL, IRJ, TIST, JASIST, JCST.