Get our free extension to see links to code for papers anywhere online!

Chrome logo Add to Chrome

Firefox logo Add to Firefox
Picture for Yelong Shen

Yelong Shen

Reader-Guided Passage Reranking for Open-Domain Question Answering


Jan 01, 2021
Yuning Mao, Pengcheng He, Xiaodong Liu, Yelong Shen, Jianfeng Gao, Jiawei Han, Weizhu Chen

Figure 1 for Reader-Guided Passage Reranking for Open-Domain Question Answering
Figure 2 for Reader-Guided Passage Reranking for Open-Domain Question Answering
Figure 3 for Reader-Guided Passage Reranking for Open-Domain Question Answering
Figure 4 for Reader-Guided Passage Reranking for Open-Domain Question Answering
* TLDR: Reranking retrieved passages by reader predictions can achieve 10~20 gains in top-1 retrieval accuracy and 1~4 gains in Exact Match (EM) without any training 
  Access Paper or Ask Questions

UnitedQA: A Hybrid Approach for Open Domain Question Answering


Jan 01, 2021
Hao Cheng, Yelong Shen, Xiaodong Liu, Pengcheng He, Weizhu Chen, Jianfeng Gao

Figure 1 for UnitedQA: A Hybrid Approach for Open Domain Question Answering
Figure 2 for UnitedQA: A Hybrid Approach for Open Domain Question Answering
Figure 3 for UnitedQA: A Hybrid Approach for Open Domain Question Answering
Figure 4 for UnitedQA: A Hybrid Approach for Open Domain Question Answering

  Access Paper or Ask Questions

NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned


Jan 01, 2021
Sewon Min, Jordan Boyd-Graber, Chris Alberti, Danqi Chen, Eunsol Choi, Michael Collins, Kelvin Guu, Hannaneh Hajishirzi, Kenton Lee, Jennimaria Palomaki, Colin Raffel, Adam Roberts, Tom Kwiatkowski, Patrick Lewis, Yuxiang Wu, Heinrich Küttler, Linqing Liu, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel, Sohee Yang, Minjoon Seo, Gautier Izacard, Fabio Petroni, Lucas Hosseini, Nicola De Cao, Edouard Grave, Ikuya Yamada, Sonse Shimaoka, Masatoshi Suzuki, Shumpei Miyawaki, Shun Sato, Ryo Takahashi, Jun Suzuki, Martin Fajcik, Martin Docekal, Karel Ondrej, Pavel Smrz, Hao Cheng, Yelong Shen, Xiaodong Liu, Pengcheng He, Weizhu Chen, Jianfeng Gao, Barlas Oguz, Xilun Chen, Vladimir Karpukhin, Stan Peshterliev, Dmytro Okhonko, Michael Schlichtkrull, Sonal Gupta, Yashar Mehdad, Wen-tau Yih

Figure 1 for NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned
Figure 2 for NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned
Figure 3 for NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned
Figure 4 for NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned
* 26 pages 
  Access Paper or Ask Questions

A Simple but Tough-to-Beat Data Augmentation Approach for Natural Language Understanding and Generation


Oct 23, 2020
Dinghan Shen, Mingzhi Zheng, Yelong Shen, Yanru Qu, Weizhu Chen

Figure 1 for A Simple but Tough-to-Beat Data Augmentation Approach for Natural Language Understanding and Generation
Figure 2 for A Simple but Tough-to-Beat Data Augmentation Approach for Natural Language Understanding and Generation
Figure 3 for A Simple but Tough-to-Beat Data Augmentation Approach for Natural Language Understanding and Generation
Figure 4 for A Simple but Tough-to-Beat Data Augmentation Approach for Natural Language Understanding and Generation
* Source code is available at: https://github.com/dinghanshen/cutoff 
  Access Paper or Ask Questions

CoDA: Contrast-enhanced and Diversity-promoting Data Augmentation for Natural Language Understanding


Oct 16, 2020
Yanru Qu, Dinghan Shen, Yelong Shen, Sandra Sajeev, Jiawei Han, Weizhu Chen

Figure 1 for CoDA: Contrast-enhanced and Diversity-promoting Data Augmentation for Natural Language Understanding
Figure 2 for CoDA: Contrast-enhanced and Diversity-promoting Data Augmentation for Natural Language Understanding
Figure 3 for CoDA: Contrast-enhanced and Diversity-promoting Data Augmentation for Natural Language Understanding
Figure 4 for CoDA: Contrast-enhanced and Diversity-promoting Data Augmentation for Natural Language Understanding

  Access Paper or Ask Questions

Improving Self-supervised Pre-training via a Fully-Explored Masked Language Model


Oct 14, 2020
Mingzhi Zheng, Dinghan Shen, Yelong Shen, Weizhu Chen, Lin Xiao

Figure 1 for Improving Self-supervised Pre-training via a Fully-Explored Masked Language Model
Figure 2 for Improving Self-supervised Pre-training via a Fully-Explored Masked Language Model
Figure 3 for Improving Self-supervised Pre-training via a Fully-Explored Masked Language Model
Figure 4 for Improving Self-supervised Pre-training via a Fully-Explored Masked Language Model

  Access Paper or Ask Questions

Generation-Augmented Retrieval for Open-domain Question Answering


Sep 17, 2020
Yuning Mao, Pengcheng He, Xiaodong Liu, Yelong Shen, Jianfeng Gao, Jiawei Han, Weizhu Chen

Figure 1 for Generation-Augmented Retrieval for Open-domain Question Answering
Figure 2 for Generation-Augmented Retrieval for Open-domain Question Answering
Figure 3 for Generation-Augmented Retrieval for Open-domain Question Answering
Figure 4 for Generation-Augmented Retrieval for Open-domain Question Answering

  Access Paper or Ask Questions

Recurrent Chunking Mechanisms for Long-Text Machine Reading Comprehension


May 19, 2020
Hongyu Gong, Yelong Shen, Dian Yu, Jianshu Chen, Dong Yu

Figure 1 for Recurrent Chunking Mechanisms for Long-Text Machine Reading Comprehension
Figure 2 for Recurrent Chunking Mechanisms for Long-Text Machine Reading Comprehension
Figure 3 for Recurrent Chunking Mechanisms for Long-Text Machine Reading Comprehension
Figure 4 for Recurrent Chunking Mechanisms for Long-Text Machine Reading Comprehension

  Access Paper or Ask Questions

MART: Memory-Augmented Recurrent Transformer for Coherent Video Paragraph Captioning


May 11, 2020
Jie Lei, Liwei Wang, Yelong Shen, Dong Yu, Tamara L. Berg, Mohit Bansal

Figure 1 for MART: Memory-Augmented Recurrent Transformer for Coherent Video Paragraph Captioning
Figure 2 for MART: Memory-Augmented Recurrent Transformer for Coherent Video Paragraph Captioning
Figure 3 for MART: Memory-Augmented Recurrent Transformer for Coherent Video Paragraph Captioning
Figure 4 for MART: Memory-Augmented Recurrent Transformer for Coherent Video Paragraph Captioning
* ACL 2020 (12 pages) 
  Access Paper or Ask Questions

A Hybrid Retrieval-Generation Neural Conversation Model


Apr 19, 2019
Liu Yang, Junjie Hu, Minghui Qiu, Chen Qu, Jianfeng Gao, W. Bruce Croft, Xiaodong Liu, Yelong Shen, Jingjing Liu

Figure 1 for A Hybrid Retrieval-Generation Neural Conversation Model
Figure 2 for A Hybrid Retrieval-Generation Neural Conversation Model
Figure 3 for A Hybrid Retrieval-Generation Neural Conversation Model
Figure 4 for A Hybrid Retrieval-Generation Neural Conversation Model
* 11 pages 
  Access Paper or Ask Questions

Unsupervised Deep Structured Semantic Models for Commonsense Reasoning


Apr 03, 2019
Shuohang Wang, Sheng Zhang, Yelong Shen, Xiaodong Liu, Jingjing Liu, Jianfeng Gao, Jing Jiang

Figure 1 for Unsupervised Deep Structured Semantic Models for Commonsense Reasoning
Figure 2 for Unsupervised Deep Structured Semantic Models for Commonsense Reasoning
Figure 3 for Unsupervised Deep Structured Semantic Models for Commonsense Reasoning
Figure 4 for Unsupervised Deep Structured Semantic Models for Commonsense Reasoning
* To appear in NAACL 2019, 10 pages 
  Access Paper or Ask Questions

StoryGAN: A Sequential Conditional GAN for Story Visualization


Dec 06, 2018
Yitong Li, Zhe Gan, Yelong Shen, Jingjing Liu, Yu Cheng, Yuexin Wu, Lawrence Carin, David Carlson, Jianfeng Gao

Figure 1 for StoryGAN: A Sequential Conditional GAN for Story Visualization
Figure 2 for StoryGAN: A Sequential Conditional GAN for Story Visualization
Figure 3 for StoryGAN: A Sequential Conditional GAN for Story Visualization
Figure 4 for StoryGAN: A Sequential Conditional GAN for Story Visualization

  Access Paper or Ask Questions

M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search


Nov 01, 2018
Yelong Shen, Jianshu Chen, Po-Sen Huang, Yuqing Guo, Jianfeng Gao

Figure 1 for M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search
Figure 2 for M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search
Figure 3 for M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search
Figure 4 for M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search
* Yelong Shen, Jianshu Chen and Po-Sen Huang contributed equally to the paper. Published at NIPS 2018 
  Access Paper or Ask Questions

Multi-Task Learning for Machine Reading Comprehension


Sep 18, 2018
Yichong Xu, Xiaodong Liu, Yelong Shen, Jingjing Liu, Jianfeng Gao

Figure 1 for Multi-Task Learning for Machine Reading Comprehension
Figure 2 for Multi-Task Learning for Machine Reading Comprehension
Figure 3 for Multi-Task Learning for Machine Reading Comprehension
Figure 4 for Multi-Task Learning for Machine Reading Comprehension
* 9 pages, 2 figures, 7 tables 
  Access Paper or Ask Questions

Language-Based Image Editing with Recurrent Attentive Models


Jun 10, 2018
Jianbo Chen, Yelong Shen, Jianfeng Gao, Jingjing Liu, Xiaodong Liu

Figure 1 for Language-Based Image Editing with Recurrent Attentive Models
Figure 2 for Language-Based Image Editing with Recurrent Attentive Models
Figure 3 for Language-Based Image Editing with Recurrent Attentive Models
Figure 4 for Language-Based Image Editing with Recurrent Attentive Models
* Accepted to CVPR 2018 as a Spotlight 
  Access Paper or Ask Questions

Stochastic Answer Networks for Machine Reading Comprehension


May 15, 2018
Xiaodong Liu, Yelong Shen, Kevin Duh, Jianfeng Gao

Figure 1 for Stochastic Answer Networks for Machine Reading Comprehension
Figure 2 for Stochastic Answer Networks for Machine Reading Comprehension
Figure 3 for Stochastic Answer Networks for Machine Reading Comprehension
Figure 4 for Stochastic Answer Networks for Machine Reading Comprehension
* 11 pages, 5 figures, Accepted to ACL 2018 
  Access Paper or Ask Questions

Link Prediction using Embedded Knowledge Graphs


Apr 22, 2018
Yelong Shen, Po-Sen Huang, Ming-Wei Chang, Jianfeng Gao

Figure 1 for Link Prediction using Embedded Knowledge Graphs
Figure 2 for Link Prediction using Embedded Knowledge Graphs
Figure 3 for Link Prediction using Embedded Knowledge Graphs
Figure 4 for Link Prediction using Embedded Knowledge Graphs

  Access Paper or Ask Questions

Dynamic Fusion Networks for Machine Reading Comprehension


Feb 26, 2018
Yichong Xu, Jingjing Liu, Jianfeng Gao, Yelong Shen, Xiaodong Liu

Figure 1 for Dynamic Fusion Networks for Machine Reading Comprehension
Figure 2 for Dynamic Fusion Networks for Machine Reading Comprehension
Figure 3 for Dynamic Fusion Networks for Machine Reading Comprehension
Figure 4 for Dynamic Fusion Networks for Machine Reading Comprehension
* 13 pages, 5 figures, 5 tables 
  Access Paper or Ask Questions

FusionNet: Fusing via Fully-Aware Attention with Application to Machine Comprehension


Feb 04, 2018
Hsin-Yuan Huang, Chenguang Zhu, Yelong Shen, Weizhu Chen

Figure 1 for FusionNet: Fusing via Fully-Aware Attention with Application to Machine Comprehension
Figure 2 for FusionNet: Fusing via Fully-Aware Attention with Application to Machine Comprehension
Figure 3 for FusionNet: Fusing via Fully-Aware Attention with Application to Machine Comprehension
Figure 4 for FusionNet: Fusing via Fully-Aware Attention with Application to Machine Comprehension
* Published in Sixth International Conference on Learning Representations (ICLR), 2018 
  Access Paper or Ask Questions

An Empirical Analysis of Multiple-Turn Reasoning Strategies in Reading Comprehension Tasks


Nov 09, 2017
Yelong Shen, Xiaodong Liu, Kevin Duh, Jianfeng Gao

Figure 1 for An Empirical Analysis of Multiple-Turn Reasoning Strategies in Reading Comprehension Tasks
Figure 2 for An Empirical Analysis of Multiple-Turn Reasoning Strategies in Reading Comprehension Tasks
Figure 3 for An Empirical Analysis of Multiple-Turn Reasoning Strategies in Reading Comprehension Tasks
Figure 4 for An Empirical Analysis of Multiple-Turn Reasoning Strategies in Reading Comprehension Tasks

  Access Paper or Ask Questions

ReasoNet: Learning to Stop Reading in Machine Comprehension


Jun 20, 2017
Yelong Shen, Po-Sen Huang, Jianfeng Gao, Weizhu Chen

Figure 1 for ReasoNet: Learning to Stop Reading in Machine Comprehension
Figure 2 for ReasoNet: Learning to Stop Reading in Machine Comprehension
Figure 3 for ReasoNet: Learning to Stop Reading in Machine Comprehension
Figure 4 for ReasoNet: Learning to Stop Reading in Machine Comprehension
* in KDD 2017 
  Access Paper or Ask Questions

Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval


Jan 16, 2016
Hamid Palangi, Li Deng, Yelong Shen, Jianfeng Gao, Xiaodong He, Jianshu Chen, Xinying Song, Rabab Ward

Figure 1 for Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval
Figure 2 for Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval
Figure 3 for Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval
Figure 4 for Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval
* To appear in IEEE/ACM Transactions on Audio, Speech, and Language Processing 
  Access Paper or Ask Questions

End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture


Nov 01, 2015
Jianshu Chen, Ji He, Yelong Shen, Lin Xiao, Xiaodong He, Jianfeng Gao, Xinying Song, Li Deng

Figure 1 for End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture
Figure 2 for End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture
Figure 3 for End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture
Figure 4 for End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture
* Proc. NIPS 2015 
  Access Paper or Ask Questions

A Deep Embedding Model for Co-occurrence Learning


Jun 04, 2015
Yelong Shen, Ruoming Jin, Jianshu Chen, Xiaodong He, Jianfeng Gao, Li Deng

Figure 1 for A Deep Embedding Model for Co-occurrence Learning
Figure 2 for A Deep Embedding Model for Co-occurrence Learning
Figure 3 for A Deep Embedding Model for Co-occurrence Learning
Figure 4 for A Deep Embedding Model for Co-occurrence Learning

  Access Paper or Ask Questions