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Feng Nan
Publications
- 2023
- [c21]Nihal Jain, Dejiao Zhang, Wasi Uddin Ahmad, Zijian Wang, Feng Nan, Xiaopeng Li, Ming Tan, Ramesh Nallapati, Baishakhi Ray, Parminder Bhatia, Xiaofei Ma, Bing Xiang:
ContraCLM: Contrastive Learning For Causal Language Model. ACL (1) 2023: 6436-6459 - [c20]Kung-Hsiang Huang, Siffi Singh, Xiaofei Ma, Wei Xiao, Feng Nan, Nicholas Dingwall, William Yang Wang, Kathleen R. McKeown:
SWING: Balancing Coverage and Faithfulness for Dialogue Summarization. EACL (Findings) 2023: 512-525 - [c19]Markus Dreyer, Mengwen Liu, Feng Nan, Sandeep Atluri, Sujith Ravi:
Evaluating the Tradeoff Between Abstractiveness and Factuality in Abstractive Summarization. EACL (Findings) 2023: 2044-2060 - [i19]Kung-Hsiang Huang, Siffi Singh, Xiaofei Ma, Wei Xiao, Feng Nan, Nicholas Dingwall, William Yang Wang, Kathleen R. McKeown:
SWING: Balancing Coverage and Faithfulness for Dialogue Summarization. CoRR abs/2301.10483 (2023) - 2022
- [c18]Peng Shi, Patrick Ng, Feng Nan, Henghui Zhu, Jun Wang, Jiarong Jiang, Alexander Hanbo Li, Rishav Chakravarti, Donald Weidner, Bing Xiang, Zhiguo Wang:
Generation-Focused Table-Based Intermediate Pre-training for Free-Form Question Answering. AAAI 2022: 11312-11320 - [c17]Fei-Tzin Lee, Miguel Ballesteros, Feng Nan, Kathleen R. McKeown:
Using Structured Content Plans for Fine-grained Syntactic Control in Pretrained Language Model Generation. COLING 2022: 5882-5895 - [i18]Nihal Jain, Dejiao Zhang, Wasi Uddin Ahmad, Zijian Wang, Feng Nan, Xiaopeng Li, Ming Tan, Ramesh Nallapati, Baishakhi Ray, Parminder Bhatia, Xiaofei Ma, Bing Xiang:
ContraGen: Effective Contrastive Learning For Causal Language Model. CoRR abs/2210.01185 (2022) - 2021
- [c16]Yifan Gao, Henghui Zhu, Patrick Ng, Cícero Nogueira dos Santos, Zhiguo Wang, Feng Nan, Dejiao Zhang, Ramesh Nallapati, Andrew O. Arnold, Bing Xiang:
Answering Ambiguous Questions through Generative Evidence Fusion and Round-Trip Prediction. ACL/IJCNLP (1) 2021: 3263-3276 - [c15]Feng Nan, Cícero Nogueira dos Santos, Henghui Zhu, Patrick Ng, Kathleen R. McKeown, Ramesh Nallapati, Dejiao Zhang, Zhiguo Wang, Andrew O. Arnold, Bing Xiang:
Improving Factual Consistency of Abstractive Summarization via Question Answering. ACL/IJCNLP (1) 2021: 6881-6894 - [c14]Feng Nan, Ramesh Nallapati, Zhiguo Wang, Cícero Nogueira dos Santos, Henghui Zhu, Dejiao Zhang, Kathy McKeown, Bing Xiang:
Entity-level Factual Consistency of Abstractive Text Summarization. EACL 2021: 2727-2733 - [c13]Dejiao Zhang, Feng Nan, Xiaokai Wei, Shang-Wen Li, Henghui Zhu, Kathleen R. McKeown, Ramesh Nallapati, Andrew O. Arnold, Bing Xiang:
Supporting Clustering with Contrastive Learning. NAACL-HLT 2021: 5419-5430 - [i17]Feng Nan, Ramesh Nallapati, Zhiguo Wang, Cícero Nogueira dos Santos, Henghui Zhu, Dejiao Zhang, Kathleen R. McKeown, Bing Xiang:
Entity-level Factual Consistency of Abstractive Text Summarization. CoRR abs/2102.09130 (2021) - [i16]Dejiao Zhang, Feng Nan, Xiaokai Wei, Shang-Wen Li, Henghui Zhu, Kathleen R. McKeown, Ramesh Nallapati, Andrew O. Arnold, Bing Xiang:
Supporting Clustering with Contrastive Learning. CoRR abs/2103.12953 (2021) - [i15]Han-Chin Shing, Chaitanya Shivade, Nima Pourdamghani, Feng Nan, Philip Resnik, Douglas W. Oard, Parminder Bhatia:
Towards Clinical Encounter Summarization: Learning to Compose Discharge Summaries from Prior Notes. CoRR abs/2104.13498 (2021) - [i14]Feng Nan, Cícero Nogueira dos Santos, Henghui Zhu, Patrick Ng, Kathleen R. McKeown, Ramesh Nallapati, Dejiao Zhang, Zhiguo Wang, Andrew O. Arnold, Bing Xiang:
Improving Factual Consistency of Abstractive Summarization via Question Answering. CoRR abs/2105.04623 (2021) - [i13]Markus Dreyer, Mengwen Liu, Feng Nan, Sandeep Atluri, Sujith Ravi:
Analyzing the Abstractiveness-Factuality Tradeoff With Nonlinear Abstractiveness Constraints. CoRR abs/2108.02859 (2021) - 2020
- [c12]Henghui Zhu, Feng Nan, Zhiguo Wang, Ramesh Nallapati, Bing Xiang:
Who Did They Respond to? Conversation Structure Modeling Using Masked Hierarchical Transformer. AAAI 2020: 9741-9748 - [c11]Dejiao Zhang, Ramesh Nallapati, Henghui Zhu, Feng Nan, Cícero Nogueira dos Santos, Kathleen R. McKeown, Bing Xiang:
Unsupervised Domain Adaptation for Cross-lingual Text Labeling. EMNLP (Findings) 2020: 3527-3536 - [c10]Siamak Shakeri, Cícero Nogueira dos Santos, Henghui Zhu, Patrick Ng, Feng Nan, Zhiguo Wang, Ramesh Nallapati, Bing Xiang:
End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systems. EMNLP (1) 2020: 5445-5460 - [i12]Siamak Shakeri, Cícero Nogueira dos Santos, Henry Zhu, Patrick Ng, Feng Nan, Zhiguo Wang, Ramesh Nallapati, Bing Xiang:
End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systems. CoRR abs/2010.06028 (2020) - [i11]Yifan Gao, Henghui Zhu, Patrick Ng, Cícero Nogueira dos Santos, Zhiguo Wang, Feng Nan, Dejiao Zhang, Ramesh Nallapati, Andrew O. Arnold, Bing Xiang:
Answering Ambiguous Questions through Generative Evidence Fusion and Round-Trip Prediction. CoRR abs/2011.13137 (2020) - 2019
- [c7]Feng Nan, Ran Ding, Ramesh Nallapati, Bing Xiang:
Topic Modeling with Wasserstein Autoencoders. ACL (1) 2019: 6345-6381 - [i10]Feng Nan, Ran Ding, Ramesh Nallapati, Bing Xiang:
Topic Modeling with Wasserstein Autoencoders. CoRR abs/1907.12374 (2019) - [i9]Henghui Zhu, Feng Nan, Zhiguo Wang, Ramesh Nallapati, Bing Xiang:
Who did They Respond to? Conversation Structure Modeling using Masked Hierarchical Transformer. CoRR abs/1911.10666 (2019) - 2017
- [j1]Feng Nan, Venkatesh Saligrama:
Comments on the Proof of Adaptive Stochastic Set Cover Based on Adaptive Submodularity and Its Implications for the Group Identification Problem in "Group-Based Active Query Selection for Rapid Diagnosis in Time-Critical Situations". IEEE Trans. Inf. Theory 63(11): 7612-7614 (2017) - [c6]Feng Nan, Venkatesh Saligrama:
Adaptive Classification for Prediction Under a Budget. NIPS 2017: 4727-4737 - [i8]Feng Nan, Venkatesh Saligrama:
Dynamic Model Selection for Prediction Under a Budget. CoRR abs/1704.07505 (2017) - [i7]Feng Nan, Venkatesh Saligrama:
Comments on the proof of adaptive submodular function minimization. CoRR abs/1705.03771 (2017) - [i6]Feng Nan, Venkatesh Saligrama:
Adaptive Classification for Prediction Under a Budget. CoRR abs/1705.10194 (2017) - [i5]Henghui Zhu, Feng Nan, Ioannis Ch. Paschalidis, Venkatesh Saligrama:
Sequential Dynamic Decision Making with Deep Neural Nets on a Test-Time Budget. CoRR abs/1705.10924 (2017) - 2016
- [c5]Feng Nan, Joseph Wang, Venkatesh Saligrama:
Pruning Random Forests for Prediction on a Budget. NIPS 2016: 2334-2342 - [i4]Feng Nan, Joseph Wang, Venkatesh Saligrama:
Optimally Pruning Decision Tree Ensembles With Feature Cost. CoRR abs/1601.00955 (2016) - [i3]Feng Nan, Joseph Wang, Venkatesh Saligrama:
Pruning Random Forests for Prediction on a Budget. CoRR abs/1606.05060 (2016) - 2015
- [c4]Feng Nan, Joseph Wang, Venkatesh Saligrama:
Feature-Budgeted Random Forest. ICML 2015: 1983-1991 - [i2]Feng Nan, Joseph Wang, Venkatesh Saligrama:
Max-Cost Discrete Function Evaluation Problem under a Budget. CoRR abs/1501.02702 (2015) - [i1]Feng Nan, Joseph Wang, Venkatesh Saligrama:
Feature-Budgeted Random Forest. CoRR abs/1502.05925 (2015) - 2014
- [c3]Feng Nan, Mohammad Moghadasi, Pirooz Vakili, Sandor Vajda, Dima Kozakov, Ioannis Ch. Paschalidis:
A Subspace Semi-Definite programming-based Underestimation (SSDU) method for stochastic global optimization in protein docking. CDC 2014: 4623-4628 - [c2]Feng Nan, Joseph Wang, Kirill Trapeznikov, Venkatesh Saligrama:
Fast margin-based cost-sensitive classification. ICASSP 2014: 2952-2956
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