Research

Talks

  1. Tutorial @NAACL’24. Spatial and Temporal Language Understanding: Representation, Reasoning, and Grounding (part II) [slides (pdf)] [video].
  2. LLM brief overview @TRB’24. [ppt][video]
  3. Tutorial @ACL’23. Indirectly Supervised Natural Language Processing [website]. Theory part [ppt][pdf].
  4. Tutorial @AAAI’21 & ACL’21. Event-Centric Natural Language Understanding. [website1, website2]
  5. TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions. EMNLP, 2020. [talk]
  6. Faculty candidate job talks, 2019. [ppt; 80MB]
  7. Job talk at AI2. Jan 25, 2019. [ppt][video]
  8. A Multi-Axis Annotation Scheme for Event Temporal Relations. ACL, 2018. [ppt]

Publications

AI

  1. Fei Wang, Chao Shang, Sarthak Jain, Shuai Wang, Qiang Ning, Bonan Min, Vittorio Castelli, Yassine Benajiba, Dan Roth. “From Instructions to Constraints: Language Model Alignment with Automatic Constraint Verification.” 2024. arxiv
  2. Ehsan Qasemi, Piyush Khanna, Qiang Ning, Muhao Chen. “PInKS: Preconditioned Commonsense Inference with Minimal Supervision.” AACL 2022. arxiv
  3. Wenxuan Zhou, Qiang Ning, Heba Elfardy, Kevin Small, Muhao Chen. “Answer Consolidation: Formulation and Benchmarking.” NAACL 2022. paper
  4. Shuaicheng Zhang, Qiang Ning, Lifu Huang. “Extracting Temporal Event Relation with Syntactic-Guided Temporal Graph Transformer.” NAACL Findings 2022. arxiv
  5. Qiang Ning, Ben Zhou, Hao Wu, Haoruo Peng, Chuchu Fan, Matt Gardner. “A Meta-framework for Spatiotemporal Quantity Extraction from Text.” ACL, 2022. talk
  6. Rujun Han, I-Hung Hsu, Jiao Sun, Julia Baylon, Qiang Ning, Dan Roth and Nanyun Peng. “ESTER: A Machine Reading Comprehension Dataset for Reasoning about Event Semantic Relations.” EMNLP, 2021. arxiv
  7. Hangfeng He, Mingyuan Zhang, Qiang Ning and Dan Roth. “Foreseeing the Benefits of Incidental Supervision.” EMNLP, 2021. arxiv slides ppt
  8. Roshanak Mirzaee, Hossein Rajaby Faghihi, Qiang Ning, and Parisa Kordjamshidi. “SPARTQA: A Textual Question Answering Benchmark for Spatial Reasoning.” NAACL, 2021. arxiv
  9. Ben Zhou, Kyle Richardson, Qiang Ning, Tushar Khot, Ashish Sabharwal, and Dan Roth. “Temporal Reasoning on Implicit Events from Distant Supervision.” NAACL, 2021. arxiv
  10. Haoyang Wen, Yanru Qu, Heng Ji, Qiang Ning, Jiawei Han, Avi Sil, Hanghang Tong, and Dan Roth. “Event Time Extraction and Propagation via Graph Attention Networks.” NAACL, 2021. paper
  11. Kaifu Wang, Qiang Ning, and Dan Roth. “Learnability with Indirect Supervision Signals.” NeurIPS, 2020. arxiv poster ppt
  12. Matt Gardner et al. “Evaluating Models’ Local Decision Boundaries via Contrast Sets.” Findings of EMNLP, 2020. arxiv
  13. Qiang Ning, Hao Wu, Rujun Han, Nanyun Peng, Matt Gardner, and Dan Roth. “TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions.” EMNLP, 2020. website dataset talk arxiv
  14. Qiang Ning, Hao Wu, Pradeep Dasigi, Dheeru Dua, Matt Gardner, Robert L. Logan IV, Ana Marasovic, and Zhen Nie. “Easy, Reproducible and Quality-Controlled Data Collection with CROWDAQ.” EMNLP, 2020 (demo paper). website arxiv
  15. Hangfeng He, Qiang Ning, and Dan Roth. “QuASE: Question-Answer Driven Sentence Encoding.” ACL, 2020. arxiv
  16. Ben Zhou, Qiang Ning, Daniel Khashabi, and Dan Roth. “Temporal Common Sense Acquisition with Minimal Supervision.” ACL, 2020. arxiv
  17. Haoruo Peng, Qiang Ning, and Dan Roth. “KnowSemLM: A Knowledge Infused Semantic Language Model.” CoNLL, 2019. [website] [talk] [github]
  18. Rujun Han, Qiang Ning, and Nanyun Peng. “Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction.” EMNLP, 2019. [pdf] [poster] [github]
  19. Ben Zhou, Daniel Khashabi, Qiang Ning, and Dan Roth. “‘Going on a vacation’ takes longer than ‘Going for a walk’: A Study of Temporal Commonsense Understanding.” EMNLP, 2019 (short paper). [website] [talk slides] [talk pdf] [github]
  20. Qiang Ning, Sanjay Subramanian, and Dan Roth. “An Improved Neural Baseline for Temporal Relation Extraction.” EMNLP, 2019 (short paper). [website] [poster] [github]
  21. Eric Graves, Qiang Ning, and Prithwish Basu. “An information theoretic model for summarization, and some basic results.” IEEE International Symposium on Information Theory (ISIT), 2019. [pdf] [slides]
    • Understanding text summarization from a perspective of information theory.
  22. Qiang Ning, Hangfeng He, Chuchu Fan, and Dan Roth. “Partial or Complete, That’s The Question.” NAACL, 2019. [website] [pdf] [poster] [ppt]
  23. Qiang Ning, Ben Zhou, Zhili Feng, Haoruo Peng, and Dan Roth. “CogCompTime: A Tool for Understanding Time in Natural Language Text.” EMNLP, 2018 (demo paper). [website] [pdf] [poster] [online demo][github].
    • A state-of-the-art automatic tool for:
    • Time expression extraction and normalization
    • Temporal relation extraction
  24. Qiang Ning, Hao Wu, and Dan Roth. “A Multi-Axis Annotation Scheme for Event Temporal Relations.” ACL, 2018. [website] [pdf] [Annotation Guidelines] [talk] [github:MATRES]
    • A new crowdsourcing annotation scheme for collecting temporal relation data more reliably and more efficiently
    • Achieved approx. 20% improvement in performance
  25. Qiang Ning, Zhili Feng, Hao Wu, and Dan Roth. “Joint Reasoning for Temporal and Causal Relations.” ACL, 2018. [website] [pdf] [talk] [github:TCR]
  26. Qiang Ning, Zhongzhi Yu, Chuchu Fan, and Dan Roth. “Exploiting Partially Annotated Data in Temporal Relation Extraction.” *SEM, 2018 (short paper). [website] [pdf] [poster] [github]
    • Incidental supervision for temporal relation extraction
  27. Qiang Ning, Hao Wu, Haoruo Peng, and Dan Roth. “Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource.” NAACL, 2018. [website] [pdf] [poster] [github:TemProb] [Download TemProb]
    • Knowledge-base encoding prior statistics
    • For example, “die” should be after “explode”, instead of before; “ask” should be before “help” instead of after
    • Mined from a million NYT news articles using Amazon Web Services (AWS)
  28. Qiang Ning, Zhili Feng, and Dan Roth. “A Structured Learning Approach to Temporal Relation Extraction.” EMNLP, 2017. [website] [pdf] [talk] [github]
    • Structured Learning
    • Constraint-Driven Learning (CoDL)
  29. Qiang Ning, Kan Chen, Li Yi, Chuchu Fan, Yao Lu, and Jiangtao Wen. “Image Super-Resolution via Analysis Sparse Prior.” IEEE Signal Processing Letters, vol. 20, no. 4, p399-402, April 2013. [pdf] [code]
    • Image reconstruction/super-resolution
    • Compressed sensing

Other areas

  1. Chuchu Fan, Zengyi Qin, Umang Mathur, Qiang Ning, Sayan Mitra, Mahesh Viswanathan. “Controller synthesis for linear system with reach-avoid specifications.” IEEE Transactions on Automatic Control, Apr 2022.
  2. Qiang Ning, Chao Ma, Fan Lam, and Zhi-Pei Liang. “Spectral Quantification for High-Resolution MR Spectroscopic Imaging with Spatiospectral Constraints.” IEEE Transactions on Biomedical Engineering, vol. 64, no. 5, p1178-1186, May 2017 (DOI: 10.1109/TBME.2016.2594583). [pdf]
    • Brain anatomy guided metabolite measuring
    • Cramer-Rao bound analysis
    • Accelerated brain imaging
  3. Chao Ma, Fan Lam, Qiang Ning, Curtis Johnson, and Zhi-Pei Liang. “High-Resolution 1H-MRSI of the Brain Using Short-TE SPICE.” Magnetic Resonance in Medicine, vol. 77, no. 2, p467-479, Feb 2017. [pdf]
  4. Qiang Ning, Chao Ma, Fan Lam, Bryan Clifford, and Zhi-Pei Liang. “Removal of Nuisance Signal from Sparsely Sampled 1H-MRSI Data Using Physics-based Spectral Bases.” 24th Annual ISMRM Scientific Meeting and Exhibition, Singapore, Singapore, May 2016. [pdf] [unsubmitted draft]
  5. Qiang Ning, Chao Ma, and Zhi-Pei Liang. “Spectral Estimation for Magnetic Resonance Spectroscopic Imaging with Spatial Sparsity Constraints.” IEEE International Symposium on Biomedical Imaging: From Nano to Macro, New York, April 2015. [pdf]
  6. Qiang Ning, Chao Ma, and Zhi-Pei Liang. “Joint Estimation of Spectral Parameters from MR Spectroscopic Imaging Data” 23nd Annual ISMRM Scientific Meeting and Exhibition, Toronto, Canada, June 2015. [pdf]
  7. Qiang Ning, Chao Ma, Curtis Johnson, and Zhi-Pei Liang. “Towards Short-TE MR Spectroscopic Imaging: Spectral Decomposition and Removal of Baseline Signals.” 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, August 2014. [pdf] [poster]

Patent

  1. CN2012105247162, granted April 17, 2013.

Technotes

  1. A brief review of basic concepts in neural nets [activation functions] [backprop explained] [entropy] [gradient descent methods] [loss functions][RNNs]
  2. Roadmap of word embedding techniques and how that leads to BERT [pdf] [pptx]
  3. A brief review of tensor decomposition [pdf]
  4. A brief review of graph2vec [pdf]
  5. A brief review of some bounds [pdf]
  6. Cramer-Rao bounds and its application to spectral quantification in MRS [pdf]
  7. A brief review of matrix derivatives and descend optimization methods [pdf]
  8. Gradient calculation for nonlinear least squares problems with complex numbers [pdf]
  9. A proof of the VARiable PROjection (VARPRO) method in Hilbert space [pdf]
  10. An introduction to one-class classification. [pdf]
  11. Qualifying Exam [pdf]

Service

Chairs

  1. ACL Rolling Review. Action editor/area chair.
  2. ICLR 2024. Area chair.
  3. AAAI 2024. Senior Program Committee Member.
  4. EMNLP 2023. Area chair.
  5. NeurIPS 2023. Area chair.
  6. ACL 2023. Information extraction track (area chair).
  7. AAAI 2023. Senior Program Committee Member.
  8. NAACL 2022. Demonstration track co-chair. Information extraction (session chair).
  9. EMNLP 2021. Information extraction track (area chair).
  10. ACL 2021. Information extraction track (area chair).
  11. NAACL 2021. Information extraction track (area chair). Machine Learning for NLP (session chair).

Program committee

  1. The Annual Meeting of the Association for Computational Linguistics (ACL)
  2. Conference on Empirical Methods in Natural Language Processing (EMNLP)
  3. The Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)
  4. Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL)
  5. AAAI Conference on Artificial Intelligence (AAAI)
  6. The International Conference on Computational Linguistics (COLING)
  7. International Conference on Language Resources and Evaluation (LREC)
  8. European Conference on Information Retrieval (ECIR)
  9. CCF International Conference on Natural Language Processing and Chinese Computing (NLPCC)
  10. Journal of Artificial Intelligence Research (JAIR)
  11. Journal of Natural Language Engineering (JNLE)
  12. Neurocomputing Journal
  13. NUSE Workshop, ACL 2020
  14. IEEE Signal Processing Letters (SPL); not active in this field anymore
  15. IEEE Transactions on Biomedical Engineering (TBME); not active in this field anymore
  16. Magnetic Resonance Imaging (MRM); not active in this field anymore