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Li Dong

4 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author4

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CL4
ORCID 0000-0003-2036-1009
same name
  • Li Dong — 4 papers
  • Li Dong — 3 papers, h 63
  • Li Dong — 1 paper, h 3
  • Li Dong — 1 paper
  • Li Dong — 1 paper, h 2
  • Li Dong — 1 paper, h 0

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

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

225 citations · 233 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CL2020★ 3 cited

Harvesting and Refining Question-Answer Pairs for Unsupervised QA

Zhongli Li, Wenhui Wang, Li Dong +2

Question Answering (QA) has shown great success thanks to the availability of large-scale datasets and the effectiveness of neural models. Recent research works have attempted to e…

cs.CL2020★ 225 cited

UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training

Hangbo Bao, Li Dong, Furu Wei +8

We propose to pre-train a unified language model for both autoencoding and partially autoregressive language modeling tasks using a novel training procedure, referred to as a pseud…

cs.CL2019★ 5 cited

Can Monolingual Pretrained Models Help Cross-Lingual Classification?

Zewen Chi, Li Dong, Furu Wei +2

Multilingual pretrained language models (such as multilingual BERT) have achieved impressive results for cross-lingual transfer. However, due to the constant model capacity, multil…

cs.CL2019

Learning A Unified Named Entity Tagger From Multiple Partially Annotated Corpora For Efficient Adaptation

Xiao Huang, Li Dong, Elizabeth Boschee +1

Named entity recognition (NER) identifies typed entity mentions in raw text. While the task is well-established, there is no universally used tagset: often, datasets are annotated…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.