activity
20152021
most citedUniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training

225 citations · 1.7k across the 46 of their papers we have counts for

collaborators

102 papers

cs.CL2021

Data Augmentation for Abstractive Query-Focused Multi-Document Summarization

Ramakanth Pasunuru, Asli Celikyilmaz, Michel Galley +4

The progress in Query-focused Multi-Document Summarization (QMDS) has been limited by the lack of sufficient largescale high-quality training datasets. We present two QMDS training…

cs.RO2021

Learning to Shift Attention for Motion Generation

You Zhou, Jianfeng Gao, Tamim Asfour

One challenge of motion generation using robot learning from demonstration techniques is that human demonstrations follow a distribution with multiple modes for one task query. Pre…

cs.CV202116 cited

Self-supervised Pre-training with Hard Examples Improves Visual Representations

Chunyuan Li, Xiujun Li, Lei Zhang +3

Self-supervised pre-training (SSP) employs random image transformations to generate training data for visual representation learning. In this paper, we first present a modeling fra…

cs.CV202160 cited

VinVL: Revisiting Visual Representations in Vision-Language Models

Pengchuan Zhang, Xiujun Li, Xiaowei Hu +5

This paper presents a detailed study of improving visual representations for vision language (VL) tasks and develops an improved object detection model to provide object-centric re…

cs.CL2021

Rider: Reader-Guided Passage Reranking for Open-Domain Question Answering

Yuning Mao, Pengcheng He, Xiaodong Liu +4

Current open-domain question answering systems often follow a Retriever-Reader architecture, where the retriever first retrieves relevant passages and the reader then reads the ret…

cs.CL2021

UnitedQA: A Hybrid Approach for Open Domain Question Answering

Hao Cheng, Yelong Shen, Xiaodong Liu +3

To date, most of recent work under the retrieval-reader framework for open-domain QA focuses on either extractive or generative reader exclusively. In this paper, we study a hybrid…