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20152023
most citedUniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training

225 citations · 1.8k across the 48 of their papers we have counts for

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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.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…

cs.CL202051 cited

Few-Shot Named Entity Recognition: A Comprehensive Study

Jiaxin Huang, Chunyuan Li, Krishan Subudhi +6

This paper presents a comprehensive study to efficiently build named entity recognition (NER) systems when a small number of in-domain labeled data is available. Based upon recent…

cs.CL20207 cited

RADDLE: An Evaluation Benchmark and Analysis Platform for Robust Task-oriented Dialog Systems

Baolin Peng, Chunyuan Li, Zhu Zhang +3

For task-oriented dialog systems to be maximally useful, it must be able to process conversations in a way that is (1) generalizable with a small number of training examples for ne…

cs.CL202037 cited

Overview of the Ninth Dialog System Technology Challenge: DSTC9

Chulaka Gunasekara, Seokhwan Kim, Luis Fernando D'Haro +36

This paper introduces the Ninth Dialog System Technology Challenge (DSTC-9). This edition of the DSTC focuses on applying end-to-end dialog technologies for four distinct tasks in…