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1702.02206
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Semi-Supervised QA with Generative Domain-Adaptive Nets
Annual Meeting of the Association for Computational Linguistics (ACL), 2017
7 February 2017
Zhilin Yang
Junjie Hu
Ruslan Salakhutdinov
William W. Cohen
OOD
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Papers citing
"Semi-Supervised QA with Generative Domain-Adaptive Nets"
50 / 97 papers shown
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12 Jun 2023
LIQUID: A Framework for List Question Answering Dataset Generation
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Annual Meeting of the Association for Computational Linguistics (ACL), 2022
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289
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QA Domain Adaptation using Hidden Space Augmentation and Self-Supervised Contrastive Adaptation
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Huimin Zeng
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234
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Evaluation of Question Answering Systems: Complexity of judging a natural language
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Zhen Yang
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Nadeesha Perera
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PReGAN: Answer Oriented Passage Ranking with Weakly Supervised GAN
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J. Nie
Yutao Zhu
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05 Jul 2022
QA Is the New KR: Question-Answer Pairs as Knowledge Bases
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Wenhu Chen
William W. Cohen
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Pat Verga
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319
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Recent Advances in Neural Text Generation: A Task-Agnostic Survey
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463
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Adapting to the Long Tail: A Meta-Analysis of Transfer Learning Research for Language Understanding Tasks
Transactions of the Association for Computational Linguistics (TACL), 2021
Aakanksha Naik
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407
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Improving Question Answering Performance Using Knowledge Distillation and Active Learning
Engineering applications of artificial intelligence (EAAI), 2021
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Can Question Generation Debias Question Answering Models? A Case Study on Question-Context Lexical Overlap
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179
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Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints
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ASAT: Adaptively Scaled Adversarial Training in Time Series
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Ruihan Bao
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291
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Interviewer-Candidate Role Play: Towards Developing Real-World NLP Systems
Neeraj Varshney
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Chitta Baral
222
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Generative Adversarial Networks: A Survey Towards Private and Secure Applications
ACM Computing Surveys (CSUR), 2021
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Zuobin Xiong
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Yi-Lun Pan
317
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07 Jun 2021
MineGAN++: Mining Generative Models for Efficient Knowledge Transfer to Limited Data Domains
International Journal of Computer Vision (IJCV), 2021
Yaxing Wang
Abel Gonzalez-Garcia
Chenshen Wu
Luis Herranz
Fahad Shahbaz Khan
Shangling Jui
Joost van de Weijer
253
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28 Apr 2021
OneStop QAMaker: Extract Question-Answer Pairs from Text in a One-Stop Approach
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Xintong Bao
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213
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Self-Teaching Machines to Read and Comprehend with Large-Scale Multi-Subject Question-Answering Data
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Substructure Substitution: Structured Data Augmentation for NLP
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Karen Livescu
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Automatic Distractor Generation for Multiple Choice Questions in Standard Tests
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240
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Xiang Yue
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Multi-Stage Pre-training for Low-Resource Domain Adaptation
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Revanth Reddy Gangi Reddy
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Avirup Sil
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221
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An Empirical Study on Neural Keyphrase Generation
North American Chapter of the Association for Computational Linguistics (NAACL), 2020
Rui Meng
Xingdi Yuan
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Adam Trischler
Daqing He
301
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Improving Machine Reading Comprehension with Contextualized Commonsense Knowledge
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Kai Sun
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Claire Cardie
370
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Accelerating Real-Time Question Answering via Question Generation
Yuwei Fang
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205
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Evaluating for Diversity in Question Generation over Text
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187
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Answering Questions on COVID-19 in Real-Time
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Minbyul Jeong
Mujeen Sung
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283
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Exploring the Vulnerability of Deep Neural Networks: A Study of Parameter Corruption
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209
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Generating Diverse and Consistent QA pairs from Contexts with Information-Maximizing Hierarchical Conditional VAEs
Annual Meeting of the Association for Computational Linguistics (ACL), 2020
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Seanie Lee
Woo Tae Jeong
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Machine Reading Comprehension: The Role of Contextualized Language Models and Beyond
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Harvesting and Refining Question-Answer Pairs for Unsupervised QA
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Self-supervised Knowledge Triplet Learning for Zero-shot Question Answering
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Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering
Annual Meeting of the Association for Computational Linguistics (ACL), 2020
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Keyphrase Generation with Cross-Document Attention
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Improving the Robustness of QA Models to Challenge Sets with Variational Question-Answer Pair Generation
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Description Based Text Classification with Reinforcement Learning
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Wei Wu
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A Survey on Machine Reading Comprehension Systems
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Self-training with Noisy Student improves ImageNet classification
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