Short Bio

Mo Yu

Research Staff Member
IBM Research AI

E-mail: yum AT us DOT ibm DOT com

Short Bio

I am Mo Yu, a Research Staff Member at AI Foundations Lab, IBM Research. My research is mainly focusing on information extraction, question answering, meta-learning and representations learning methods for NLP.

I received my PhD degree from Harbin Institute of Technology in Jan 2016. Prior to IBM, I was a visiting student at Johns Hopkins University during 2014-2015 working with Prof. Mark Dredze and Prof. Raman Arora. I was visiting Baidu NLP group during the year 2012-2013; and was visiting Microsoft Research Asia in the year 2008 and 2010. I have a broad range interests on Natural Language Processing and Machine Learning, such as semantic/syntactic analysis of texts and semi-supervised learning.

  • Ph.D. in Computer Science, Harbin Institute of Technology (Jan 2016)
    • Thesis: Modeling and Learning of Distributed Representations for Natural Language Structures.
    • Advisor: Tiejun Zhao
  • M.S. in Computer Science, Harbin Institute of Technology (Jul 2011)
    • Advisor: Tiejun Zhao
  • B.S. in Computer Science, Harbin Institute of Technology (Jul 2009)

Research Experience
  • Research Staff Member, IBM Research AI (02/2017 – Present)
  • Research Staff Member, IBM Watson (03/2016 – 02/2017)
    • Research staff member in the Group of Statistical Learning for Question Answering & Discovery.
    • Team lead of the KB-QA team.
  • Research Assistant, The Center for Language and Speech Processing, Johns Hopkins University (12/2013 – 06/2015)
    • Visiting scholar at JHU working on learning structured representations for NLP. My advisors are Prof. Mark Dredze and Prof. Raman Arora.
  • Intern, Natural Language Processing Group, Baidu Inc. (05/2012 – 12/2013)
    • Research intern working on Dependency Parsing, learning of syntactic representations and online learning algorithms for problems with structured predictions. Some of my work was advised by Prof. Tong Zhang.
  • Intern, Natural Language Computation Group, MSRA. (07/2010 – 12/2010)
  • Intern, Web Search and Mining Group, MSRA. (12/2008 – 8/2009)

Selected Publications

Google Scholar Profile

Full List of My Publications (By Year)

Question Answering

  • S. Wang*, M. Yu*, T. Klinger, W. Zhang, X. Guo, S. Chang, Z. Wang, J. Jiang, G. Tesauro, M. Campbell . ``Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering''. ICLR 2018. 
  • S. Wang, M. Yu, X. Guo, Z. Wang, T. Klinger, W. Zhang, S. Chang, G. Tesauro, B. Zhou. ``R$^ 3$: Reinforced Reader-Ranker for Open-Domain Question Answering''. AAAI 2018. 
  • M. Yu, W. Yin, K. Hasan, C. dos Santos, B. Xiang, B. Zhou. ``Improved Neural Relation Detection for Knowledge Base Question Answering''. ACL 2017. 
  • W. Yin, M. Yu, B. Xiang, B. Zhou, H. Schütze. ``Simple question answering by attentive convolutional neural network''. COLING 2016.

Representation Learning for NLP

  • Z. Lin, M. Feng, CN. Santos, M. Yu, B. Xiang, B. Zhou, Y. Bengio. ``A structured self-attentive sentence embedding''. ICLR 2017.
  • M. Yu, M. Dredze, R. Arora, M. Gormley. ``Embedding Lexical Features via Low-rank Tensors''. NAACL 2016. 
  • M. Yu, M. Dredze. ``Learning Composition Models for Phrase Embeddings''. TACL, 2015.
  • M. Gormley*, M. Yu*, M. Dredze. ``Improved Relation Extraction with Feature-rich Compositional Embedding Models''. EMNLP, 2015.
  • M. Yu, M. Dredze. ``Improving Lexical Embeddings with Semantic Knowledge''. ACL 2014.
  • M. Yu, T. Zhao, D. Dong, H. Tian, D. Yu. ``Compound Embedding Features for Semi-supervised Learning''. NAACL 2013.

Machine Learning

  • M. Yu*, X. Guo*, J. Yi*, S. Chang, S. Potdar, Y. Cheng, G. Tesauro, H. Wang, B. Zhou. ``Diverse Few-Shot Text Classification with Multiple Metrics''. NAACL 2018. 
  • T. Zhao*, M. Yu*, Y. Wang, R. Arora, H. Liu. ``Accelerated Mini-batch Randomized Block Coordinate Descent Method''. NIPS 2014.

Other NLP Tasks

  • Y. Bao, S. Chang, M. Yu, R. Barzilay. ``Deriving Machine Attention from Human Rationales''. EMNLP 2018. 
  • M. Yu, T. Zhao, Y. Bai. ``Learning Domain Differences Automatically for Dependency Parsing Adaptation''. IJCAI, 2013.
  • L. Jiang, M. Yu, M. Zhou, X. Liu, T. Zhao. Target-dependent Twitter Sentiment Classification. ACL 2011.

Professional Services
  • Reviewer: ACL 2017, 2018, 2019; NAACL 2016, 2018; EMNLP 2015, 2017, 2018; ICLR 2018, 2019; NIPS 2017, 2018; AAAI 2017, 2018; IJCAI 2017, 2018, 2019
  • Senior PC Member: AAAI 2019
  • Area Chair: CCL 2017; NAACL 2019