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Mengyuan Zhou

4 papers hereh-index 681 citations11 works total

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

author position
  • first author1
  • middle author2
  • last author1

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

fields
  • cs.AI3
  • cs.CL1
same name
  • Mengyuan Zhou — 2 papers
  • Mengyuan Zhou — 1 paper
  • Mengyuan Zhou — 1 paper, h 2

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 citedSequential Attention Module for Natural Language Processing

2 citations · 3 across the 2 of their papers we have counts for

collaborators

4 papers

cs.CL2022★ 1 cited

VarMAE: Pre-training of Variational Masked Autoencoder for Domain-adaptive Language Understanding

Dou Hu, Xiaolong Hou, Xiyang Du +4

Pre-trained language models have achieved promising performance on general benchmarks, but underperform when migrated to a specific domain. Recent works perform pre-training from s…

cs.AI2021★ 2 cited

Sequential Attention Module for Natural Language Processing

Mengyuan Zhou, Jian Ma, Haiqin Yang +2

Recently, large pre-trained neural language models have attained remarkable performance on many downstream natural language processing (NLP) applications via fine-tuning. In this p…

cs.AI2021

Sattiy at SemEval-2021 Task 9: An Ensemble Solution for Statement Verification and Evidence Finding with Tables

Xiaoyi Ruan, Meizhi Jin, Jian Ma +4

Question answering from semi-structured tables can be seen as a semantic parsing task and is significant and practical for pushing the boundary of natural language understanding. E…

cs.AI2021

MagicPai at SemEval-2021 Task 7: Method for Detecting and Rating Humor Based on Multi-Task Adversarial Training

Jian Ma, Shuyi Xie, Haiqin Yang +4

This paper describes MagicPai's system for SemEval 2021 Task 7, HaHackathon: Detecting and Rating Humor and Offense. This task aims to detect whether the text is humorous and how h…

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