Report on the SIGIR 2016 Workshop on Neural Information Retrieval (Neu-IR)

The SIGIR 2016 workshop on Neural Information Retrieval (Neu-IR) took place on 21 July, 2016 in Pisa. The goal of the Neu-IR (pronounced "New IR") workshop was to serve as a forum for academic and industrial researchers, working at the intersection of information retrieval (IR) and machine learning, 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. In total, 19 papers were presented, including oral and poster presentations. The workshop program also included a session on invited "lightning talks" to encourage participants to share personal insights and negative results with the community. The workshop was well-attended with more than 120 registrations.

[1]  M. de Rijke,et al.  Siamese CBOW: Optimizing Word Embeddings for Sentence Representations , 2016, ACL.

[2]  Laure Soulier,et al.  Toward a Deep Neural Approach for Knowledge-Based IR , 2016, SIGIR 2016.

[3]  Manoj Kumar Chinnakotla,et al.  Deep Feature Fusion Network for Answer Quality Prediction in Community Question Answering , 2016, ArXiv.

[4]  Bhaskar Mitra,et al.  Exploring Session Context using Distributed Representations of Queries and Reformulations , 2015, SIGIR.

[5]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[6]  Larry P. Heck,et al.  Learning deep structured semantic models for web search using clickthrough data , 2013, CIKM.

[7]  Yonghui Wu,et al.  Exploring the Limits of Language Modeling , 2016, ArXiv.

[8]  Bhaskar Mitra,et al.  A Dual Embedding Space Model for Document Ranking , 2016, ArXiv.

[9]  Abhay Prakash,et al.  Emulating Human Conversations using Convolutional Neural Network-based IR , 2016, ArXiv.

[10]  Mandar Mitra,et al.  Word Embedding based Generalized Language Model for Information Retrieval , 2015, SIGIR.

[11]  Jakob Grue Simonsen,et al.  Deep Learning Relevance: Creating Relevant Information (as Opposed to Retrieving it) , 2016, ArXiv.

[12]  Bhaskar Mitra,et al.  Neu-IR: The SIGIR 2016 Workshop on Neural Information Retrieval , 2016, SIGIR.

[13]  Gareth J. F. Jones,et al.  Representing Documents and Queries as Sets of Word Embedded Vectors for Information Retrieval , 2016, ArXiv.

[14]  Quoc V. Le,et al.  A Neural Conversational Model , 2015, ArXiv.

[15]  Jun Wang,et al.  Learning text representation using recurrent convolutional neural network with highway layers , 2016, SIGIR 2016.

[16]  Alessandro Moschitti,et al.  Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks , 2015, SIGIR.

[17]  M. de Rijke,et al.  Short Text Similarity with Word Embeddings , 2015, CIKM.

[18]  Tara N. Sainath,et al.  Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups , 2012, IEEE Signal Processing Magazine.

[19]  Craig MacDonald,et al.  Using word embeddings in Twitter election classification , 2016, Information Retrieval Journal.

[20]  William Lewis,et al.  Skype Translator: Breaking down language and hearing barriers. A behind the scenes look at near real-time speech translation , 2015, TC.

[21]  M. de Rijke,et al.  Learning Latent Vector Spaces for Product Search , 2016, CIKM.

[22]  Markus Koskela,et al.  LSTM-Based Predictions for Proactive Information Retrieval , 2016, SIGIR 2016.

[23]  Maarten de Rijke,et al.  A Context-aware Time Model for Web Search , 2016, SIGIR.

[24]  Marcel Worring,et al.  Unsupervised, Efficient and Semantic Expertise Retrieval , 2016, WWW.

[25]  Xueqi Cheng,et al.  A Study of MatchPyramid Models on Ad-hoc Retrieval , 2016, ArXiv.

[26]  Samy Bengio,et al.  Show and tell: A neural image caption generator , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[27]  W. Bruce Croft,et al.  Adaptability of Neural Networks on Varying Granularity IR Tasks , 2016, ArXiv.

[28]  Utpal Garain,et al.  Using Word Embeddings for Automatic Query Expansion , 2016, ArXiv.

[29]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[30]  Allan Hanbury,et al.  Uncertainty in Neural Network Word Embedding: Exploration of Threshold for Similarity , 2016, ArXiv.

[31]  Craig MacDonald,et al.  Modelling User Preferences using Word Embeddings for Context-Aware Venue Recommendation , 2016, ArXiv.

[32]  Philippe Mulhem,et al.  Toward Word Embedding for Personalized Information Retrieval , 2016, SIGIR 2016.

[33]  Geoffrey Zweig,et al.  From captions to visual concepts and back , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[34]  M. de Rijke,et al.  A Neural Click Model for Web Search , 2016, WWW.

[35]  James P. Callan,et al.  Learning to Reweight Terms with Distributed Representations , 2015, SIGIR.

[36]  Bhaskar Mitra,et al.  Query Auto-Completion for Rare Prefixes , 2015, CIKM.

[37]  Georgios Balikas,et al.  An empirical study on large scale text classification with skip-gram embeddings , 2016, ArXiv.

[38]  Nick Craswell,et al.  Query Expansion with Locally-Trained Word Embeddings , 2016, ACL.

[39]  Gang Wang,et al.  Selective Term Proximity Scoring Via BP-ANN , 2016, ArXiv.

[40]  Bhaskar Mitra,et al.  Improving Document Ranking with Dual Word Embeddings , 2016, WWW.

[41]  Thomas B. Moeslund,et al.  Learning Dynamic Classes of Events using Stacked Multilayer Perceptron Networks , 2016, SIGIR 2016.

[42]  Andrea Esuli,et al.  Picture it in your mind: generating high level visual representations from textual descriptions , 2016, Information Retrieval Journal.

[43]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.