Report on the Second SIGIR Workshop on Neural Information Retrieval (Neu-IR'17)

The second SIGIR workshop on neural information retrieval (Neu-IR?17) took place on August 11, 2017, in Tokyo, Japan. Following the successful 2016 edition, the workshop continued to serve as a forum for academic and industrial researchers to present new work on neural methods for retrieval. In addition, a special track was organized focusing on resources for evaluation and reproducibility, including proposals for public benchmarking datasets and shared model repositories. A total of 19 papers?which included five special track papers? were presented in the form of oral or poster presentations. Organizers of four of the TREC 2017 tracks were invited to present at the workshop on how these IR tasks may be suitable for evaluating recent data-hungry neural approaches. The full-day workshop?with more than 170 registrants?concluded with an engaging panel discussion.

[1]  Maarten de Rijke,et al.  Thread Reconstruction in Conversational Data using Neural Coherence Models , 2017, ArXiv.

[2]  Bhaskar Mitra,et al.  Benchmark for Complex Answer Retrieval , 2017, ICTIR.

[3]  Zhengdong Lu,et al.  Deep Learning for Information Retrieval , 2016, SIGIR.

[4]  Jimmy J. Lin,et al.  Exploring the Effectiveness of Convolutional Neural Networks for Answer Selection in End-to-End Question Answering , 2017, ArXiv.

[5]  Kripabandhu Ghosh,et al.  Microblog Retrieval for Post-Disaster Relief: Applying and Comparing Neural IR Models , 2017, ArXiv.

[6]  Luo Si,et al.  Session-aware Information Embedding for E-commerce Product Recommendation , 2017, CIKM.

[7]  Bhaskar Mitra,et al.  Neural Networks for Information Retrieval , 2017, SIGIR.

[8]  Nick Craswell,et al.  Learning to Match using Local and Distributed Representations of Text for Web Search , 2016, WWW.

[9]  Maarten de Rijke,et al.  Share your Model instead of your Data: Privacy Preserving Mimic Learning for Ranking , 2017, ArXiv.

[10]  Bhaskar Mitra,et al.  Toward Incorporation of Relevant Documents in word2vec , 2017, ArXiv.

[11]  Andrew Yates,et al.  An Approach for Weakly-Supervised Deep Information Retrieval , 2017, ArXiv.

[12]  Jimmy Lin,et al.  An Exploration of Approaches to Integrating Neural Reranking Models in Multi-Stage Ranking Architectures , 2017, ArXiv.

[13]  Bhaskar Mitra,et al.  SIGIR 2017 Workshop on Neural Information Retrieval (Neu-IR'17) , 2017, SIGIR.

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

[15]  Xueqi Cheng,et al.  MatchZoo: A Toolkit for Deep Text Matching , 2017, ArXiv.

[16]  Gerard de Melo,et al.  RE-PACRR: A Context and Density-Aware Neural Information Retrieval Model , 2017, ArXiv.

[17]  Kyunghyun Cho,et al.  Task-Oriented Query Reformulation with Reinforcement Learning , 2017, EMNLP.

[18]  Bhaskar Mitra,et al.  Neural Text Embeddings for Information Retrieval , 2017, WSDM.

[19]  Bhaskar Mitra,et al.  Neural Models for Information Retrieval , 2017, ArXiv.

[20]  W. Bruce Croft,et al.  Neural Matching Models for Question Retrieval and Next Question Prediction in Conversation , 2017, ArXiv.

[21]  Yelong Shen,et al.  Learning semantic representations using convolutional neural networks for web search , 2014, WWW.

[22]  Maarten de Rijke,et al.  Semantic Entity Retrieval Toolkit , 2017, ArXiv.

[23]  Jimmy J. Lin,et al.  Integrating Lexical and Temporal Signals in Neural Ranking Models for Searching Social Media Streams , 2017, ArXiv.

[24]  Md. Mustafizur Rahman,et al.  Neural information retrieval: at the end of the early years , 2017, Information Retrieval Journal.

[25]  Andrew Yates,et al.  DE-PACRR: Exploring Layers Inside the PACRR Model , 2017, ArXiv.

[26]  Xueqi Cheng,et al.  A Deep Investigation of Deep IR Models , 2017, ArXiv.

[27]  Bhaskar Mitra,et al.  Report on the SIGIR 2016 Workshop on Neural Information Retrieval (Neu-IR) , 2016, SIGIR Forum.

[28]  Yelong Shen,et al.  ReasoNet: Learning to Stop Reading in Machine Comprehension , 2016, CoCo@NIPS.

[29]  Puneet Agrawal,et al.  A Sentiment-and-Semantics-Based Approach for Emotion Detection in Textual Conversations , 2017, ArXiv.

[30]  M. de Rijke,et al.  Modeling Label Ambiguity for Neural List-Wise Learning to Rank , 2017, ArXiv.

[31]  Amit Srivastava,et al.  Towards Semantic Query Segmentation , 2017, ArXiv.