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Helen M. Meng

25 papers hereh-index 18694 citations43 works total

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

author position
  • middle author4
  • last author21

Across the 25 of 25 papers where every author was matched, so the position is known.

fields
  • eess.AS11
  • cs.SD8
  • cs.CL4
  • cs.LG1
  • cs.MM1
same name
  • Helen M. Meng — 33 papers, h 11
  • Helen M. Meng — 8 papers, h 5
  • Helen M. Meng — 7 papers, h 10
  • Helen M. Meng — 7 papers, h 3
  • Helen M. Meng — 7 papers, h 4
  • Helen M. Meng — 5 papers, h 4

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

activity
20212023
most citedInterpretable Unified Language Checking

8 citations · 20 across the 25 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2023★ 8 cited

Interpretable Unified Language Checking

Tianhua Zhang, Hongyin Luo, Yung-Sung Chuang +7

Despite recent concerns about undesirable behaviors generated by large language models (LLMs), including non-factual, biased, and hateful language, we find LLMs are inherent multi-…

cs.CL2022

Exploiting prompt learning with pre-trained language models for Alzheimer's Disease detection

Yi Wang, Jiajun Deng, Tianzi Wang +4

Early diagnosis of Alzheimer's disease (AD) is crucial in facilitating preventive care and to delay further progression. Speech based automatic AD screening systems provide a non-i…

cs.CL2022★ 1 cited

Bayesian Neural Network Language Modeling for Speech Recognition

Boyang Xue, Shoukang Hu, Junhao Xu +3

State-of-the-art neural network language models (NNLMs) represented by long short term memory recurrent neural networks (LSTM-RNNs) and Transformers are becoming highly complex. Th…

cs.CL2021★ 1 cited

Mixed Precision of Quantization of Transformer Language Models for Speech Recognition

Junhao Xu, Shoukang Hu, Jianwei Yu +2

State-of-the-art neural language models represented by Transformers are becoming increasingly complex and expensive for practical applications. Low-bit deep neural network quantiza…

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