{"id":231283,"date":"2016-05-12T11:52:39","date_gmt":"2016-05-12T18:52:39","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-event&p=231283"},"modified":"2025-08-06T12:01:09","modified_gmt":"2025-08-06T19:01:09","slug":"neuir2016","status":"publish","type":"msr-event","link":"https:\/\/www.microsoft.com\/en-us\/research\/event\/neuir2016\/","title":{"rendered":"Neu-IR: The SIGIR 2016 Workshop on Neural Information Retrieval"},"content":{"rendered":"\n\n

<<\u00a0Final Workshop Report (opens in new tab)<\/span><\/a>\u00a0>><\/strong><\/p>\n

Submission Deadline<\/strong>: May 16
\nAcceptance Notifications<\/strong>: June 6
\nCamera-ready Deadline<\/strong>: June 17
\nWorkshop<\/strong>: July 21<\/p>\n

The first international Neu-IR (pronounced “new<\/em> IR”) workshop on neural information retrieval will be hosted at SIGIR 2016 (opens in new tab)<\/span><\/a>in Pisa, Tuscany, Italy on 21 July, 2016.Opens in a new tab<\/span><\/p>\n

(The final report on the workshop is available here (opens in new tab)<\/span><\/a>.)<\/p>\n

In recent years, deep neural networks have yielded significant performance improvements on speech recognition and computer vision tasks, as well as led to exciting breakthroughs in novel application areas such as automatic voice translation, image captioning, and conversational agents. Despite demonstrating good performance on natural language processing (NLP) tasks, the performance of deep neural networks on IR tasks has had relatively less scrutiny.<\/p>\n

The lack of many positive results in the area of information retrieval is partially due to the fact that IR tasks such as ranking are fundamentally different from NLP tasks, but also because the IR and neural network communities are only beginning to focus on the application of these techniques to core information retrieval problems. Given that deep learning has made such a big impact, first on speech processing and computer vision and now, increasingly, also on computational linguistics, it seems clear that deep learning will have a major impact on information retrieval and that this is an ideal time for a workshop in this area. Our focus is on the applicability of deep neural networks to information retrieval: demonstrating performance improvements on public or private information retrieval datasets, identifying key modelling challenges and best practices, and thinking about what insights deep neural network architectures give us about information retrieval problems.<\/p>\n

Neu-IR 2016 <\/strong>will be a highly interactive full day workshop that will provide a forum for academic and industrial researchers working at the intersection of IR and neural networks. The purpose is to provide an opportunity for people to present new work and early results, compare notes on neural network toolkits, share best practices, and discuss the main challenges facing this line of research.<\/p>\n

Please use the tabs above to navigate to see the program, the accepted papers and other details of this workshop.<\/p>\n

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Neu-IR will be a highly interactive full day workshop, featuring a mix of presentation and interaction formats. The full schedule is presented below.<\/p>\n

Morning Session I<\/strong>
\n09:00 \u2013 10:30<\/span><\/p>\n

Welcome and opening announcements [slides (opens in new tab)<\/span><\/a>]
\nBhaskar Mitra
\n15 mins<\/span><\/p>\n

Keynote: Recurrent Networks and Beyond [slides (opens in new tab)<\/span><\/a>]
\nTomas Mikolov, Facebook AI Research
\n45 mins<\/span><\/p>\n

Paper: Query Expansion with Locally-Trained Word Embeddings [slides (opens in new tab)<\/span><\/a>]
\nFernando Diaz, Bhaskar Mitra and Nick Craswell
\n15 mins<\/span><\/p>\n

Paper: Uncertainty in Neural Network Word Embedding Exploration of Potential Threshold [slides (opens in new tab)<\/span><\/a>]
\nNavid Rekabsaz, Mihai Lupu and Allan Hanbury
\n15 mins<\/span><\/p>\n

Coffee Break<\/strong>
\n10:30 \u2013 11:00<\/span><\/p>\n

Morning Session II<\/strong>
\n11:00 \u2013 12:30<\/span><\/p>\n

Lessons from the Trenches [slides (opens in new tab)<\/span><\/a>]
\n45 mins<\/span><\/p>\n

Poster presentations
\n45 mins<\/span><\/p>\n

Lunch Break<\/strong>
\n12:30 \u2013 14:00<\/span><\/p>\n

Afternoon Session I<\/strong>
\n14:00 \u2013 15:30<\/span><\/p>\n

Keynote: Does IR Need Deep Learning? [slides (opens in new tab)<\/span><\/a>]
\nHang Li, Huawei Technologies
\n45 mins<\/span><\/p>\n

Paper: Modelling User Preferences using Word Embeddings for Context-Aware Venue Recommendation [slides (opens in new tab)<\/span><\/a>]
\nJarana Manotumruksa, Craig Macdonald and Iadh Ounis
\n15 mins<\/span><\/p>\n

Paper: A Study of MatchPyramid Models on Ad-hoc Retrieval [slides (opens in new tab)<\/span><\/a>]
\nLiang Pang, Yanyan Lan, Jiafeng Guo, Jun Xu and Xueqi Cheng
\n15 mins<\/span><\/p>\n

Paper: Emulating Human Conversations using Convolutional Neural Network-based IR [slides (opens in new tab)<\/span><\/a>]
\nAbhay Prakash, Chris Brockett and Puneet Agrawal
\n15 mins<\/span><\/p>\n

Coffee Break<\/strong>
\n15:30 \u2013 16:00<\/span><\/p>\n

Afternoon Session II<\/strong>
\n16:00 \u2013 17:45<\/span><\/p>\n

Breakout session
\n45 mins<\/span><\/p>\n

Breakout session retrospective
\n45 mins<\/span><\/p>\n

Concluding remarks
\n15 mins<\/span><\/p>\n

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Recurrent Networks and Beyond<\/h3>\n

Tomas Mikolov, Facebook AI Research<\/p>\n

\"mikolov\" (opens in new tab)<\/span><\/a>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.<\/p>\n

Bio: Tomas Mikolov (opens in new tab)<\/span><\/a>\u00a0is a research scientist at Facebook AI Research since May 2014. Previously\u00a0he has been a member of the Google Brain team, where\u00a0he 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\u00a0his work on recurrent neural network based language models (RNNLM).\u00a0His long term research goal is to develop intelligent machines capable of learning and communication with people using natural language.<\/p>\n

 <\/p>\n

Does IR Need Deep Learning?<\/h3>\n

Hang Li, Huawei Technologies<\/p>\n

\"HangLi\" (opens in new tab)<\/span><\/a>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.<\/p>\n

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\u2019s 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.<\/p>\n

Bio: Hang Li (opens in new tab)<\/span><\/a> is director of the Noah\u2019s 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\u2019 papers received the SIGKDD\u201908 best application paper award, the SIGIR\u201908 best student paper award, the ACL\u201912 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.<\/p>\n

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We had 27 submissions (excluding three incomplete submissions). Every paper was reviewed by at least two members of the program committee and finally 19 submission were accepted\u00a0(acceptance rate of 73%). Among the accepted papers, there were a few popular themes. 8 papers were related to learning and applications of word embeddings. 10 papers focused on applications of deep neural networks for different IR tasks. The accepted papers also covered a broad range\u00a0of tasks, including question\/answering, proactive IR, knowledge-based IR,\u00a0conversational models and\u00a0text-to-image, but document ranking was a popular choice with 7 papers using it as the evaluation task.\u00a0The word cloud summary (generated using http:\/\/www.wordle.net (opens in new tab)<\/span><\/a>)\u00a0of the abstracts of the accepted papers highlights additional themes across all the submissions.<\/p>\n

\"wordcloud-abstracts\"<\/p>\n

Geographically, the accepted papers (based on the first author) ranged from\u00a09 countries and 3 continents (FR: 4, IN: 4, CN: 2, DK: 2, UK: 2, US: 2, AT: 1, FI: 1 and IT: 1). Based on the first author’s affiliation, 2 of the accepted papers came from the industry and the rest from academia.<\/p>\n

 <\/p>\n

The full list of accepted papers is below:<\/p>\n

An empirical study on large scale text classification with skip-gram embeddings\u00a0\"Pdf_icon\" (opens in new tab)<\/span><\/a>
\nGeorgios Balikas and Massih-Reza Amini<\/span><\/p>\n

Deep Feature Fusion Network for Answer Quality Prediction in Community Question Answering \"Pdf_icon\" (opens in new tab)<\/span><\/a>
\nSai Praneeth Suggu, Kushwanth N. Goutham T, Manoj K. Chinnakotla and Manish Shrivastava<\/span><\/p>\n

Selective Term Proximity Scoring Via BP-ANN\u00a0\"Pdf_icon\" (opens in new tab)<\/span><\/a>
\nJu Yang, Rebecca Stones, Gang Wang and Xiaoguang Liu<\/span><\/p>\n

Adaptability of Neural Networks on Varying Granularity IR Tasks\u00a0\"Pdf_icon\" (opens in new tab)<\/span><\/a>
\nDaniel Cohen, Qingyao Ai and W. Bruce Croft<\/span><\/p>\n

Emulating Human Conversations using Convolutional Neural Network-based IR\u00a0\"Pdf_icon\" (opens in new tab)<\/span><\/a>
\nAbhay Prakash, Chris Brockett and Puneet Agrawal<\/span><\/p>\n

A Study of MatchPyramid Models on Ad-hoc Retrieval\u00a0\"Pdf_icon\" (opens in new tab)<\/span><\/a>
\nLiang Pang, Yanyan Lan, Jiafeng Guo, Jun Xu and Xueqi Cheng<\/span><\/p>\n

Learning text representation using recurrent convolutional neural network with highway layers\u00a0\"Pdf_icon\" (opens in new tab)<\/span><\/a>
\nYing Wen, Weinan Zhang, Rui Luo and Jun Wang<\/span><\/p>\n

Toward Word Embedding for Personalized Information Retrieval\u00a0\"Pdf_icon\" (opens in new tab)<\/span><\/a>
\nNawal Ould Amer, Philippe Mulhem and Mathias G\u00e9ry<\/span><\/p>\n

Toward a Deep Neural Approach for Knowledge-Based IR\u00a0\"Pdf_icon\" (opens in new tab)<\/span><\/a>
\nGia-Hung Nguyen, Lynda Tamine, Laure Soulier and Nathalie Bricon-Souf<\/span><\/p>\n

Query Expansion with Locally-Trained Word Embeddings\u00a0\"Pdf_icon\" (opens in new tab)<\/span><\/a>
\nFernando Diaz, Bhaskar Mitra and Nick Craswell<\/span><\/p>\n

LSTM-Based Predictions for Proactive Information Retrieval\u00a0\"Pdf_icon\" (opens in new tab)<\/span><\/a>
\nPetri Luukkonen, Markus Koskela and Patrik Flor\u00e9en<\/span><\/p>\n

Picture It In Your Mind: Generating High Level Visual Representations From Textual Descriptions \"Pdf_icon\" (opens in new tab)<\/span><\/a>
\nFabio Carrara, Andrea Esuli, Tiziano Fagni, Fabrizio Falchi and Alejandro Moreo Fern\u00e1ndez<\/span><\/p>\n

Uncertainty in Neural Network Word Embedding Exploration of Potential Threshold\u00a0\"Pdf_icon\" (opens in new tab)<\/span><\/a>
\nNavid Rekabsaz, Mihai Lupu and Allan Hanbury<\/span><\/p>\n

Deep Learning Relevance: Creating Relevant Information (as Opposed to Retrieving it) \"Pdf_icon\" (opens in new tab)<\/span><\/a>
\nChristina Lioma, Birger Larsen, Casper Petersen and Jakob Grue Simonsen<\/span><\/p>\n

Learning Dynamic Classes of Events using Stacked Multilayer Perceptron Networks\u00a0\"Pdf_icon\" (opens in new tab)<\/span><\/a>
\nNattiya Kanhabua, Huamin Ren and Thomas B. Moeslund<\/span><\/p>\n

Representing Documents and Queries as Sets of Word Embedded Vectors for Information Retrieval\u00a0\"Pdf_icon\" (opens in new tab)<\/span><\/a>
\nDebasis Ganguly, Dwaipayan Roy, Mandar Mitra and Gareth Jones<\/span><\/p>\n

Using Word Embeddings for Automatic Query Expansion\u00a0\"Pdf_icon\" (opens in new tab)<\/span><\/a>
\nDwaipayan Roy, Debjyoti Paul and Mandar Mitra<\/span><\/p>\n

Modelling User Preferences using Word Embeddings for Context-Aware Venue Recommendation\u00a0\"Pdf_icon\" (opens in new tab)<\/span><\/a>
\nJarana Manotumruksa, Craig Macdonald and Iadh Ounis<\/span><\/p>\n

Using Word Embeddings in Twitter Election Classification\u00a0\"Pdf_icon\" (opens in new tab)<\/span><\/a>
\nXiao Yang, Craig Macdonald and Iadh Ounis<\/span><\/p>\n

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The Lessons from the Trenches will be a series of “lightning talks” by\u00a0researchers\u00a0who are actively working in the intersection of information retrieval and neural networks who\u00a0want to share their personal insights and learning with the broader community. In particular, we are hoping to hear about,<\/p>\n