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Di Liang

11 papers hereh-index 14498 citations18 works total

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

author position
  • first author1
  • middle author8
  • last author1

Across the 10 of 11 papers where every author was matched, so the position is known.

fields
  • cs.CL9
  • cs.SD1
  • eess.AS1
same name
  • Di Liang — 13 papers, h 5
  • Di Liang — 7 papers, h 4
  • Di Liang — 5 papers, h 3
  • Di Liang — 5 papers, h 4
  • Di Liang — 1 paper, h 1
  • Di Liang — 1 paper, h 1

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
20222026
most citedRobust Lottery Tickets for Pre-trained Language Models

17 citations · 38 across the 10 of their papers we have counts for

collaborators
Showing 2022Show all

4 papers · 1 filter

cs.CL2022★ 17 cited

Robust Lottery Tickets for Pre-trained Language Models

Rui Zheng, Rong Bao, Yuhao Zhou +6

Recent works on Lottery Ticket Hypothesis have shown that pre-trained language models (PLMs) contain smaller matching subnetworks(winning tickets) which are capable of reaching acc…

cs.CL2022★ 1 cited

Improving Semantic Matching through Dependency-Enhanced Pre-trained Model with Adaptive Fusion

Jian Song, Di Liang, Rumei Li +5

Transformer-based pre-trained models like BERT have achieved great progress on Semantic Sentence Matching. Meanwhile, dependency prior knowledge has also shown general benefits in…

cs.CL2022★ 6 cited

DABERT: Dual Attention Enhanced BERT for Semantic Matching

Sirui Wang, Di Liang, Jian Song +2

Transformer-based pre-trained language models such as BERT have achieved remarkable results in Semantic Sentence Matching. However, existing models still suffer from insufficient a…

cs.CL2022★ 1 cited

Searching for Optimal Subword Tokenization in Cross-domain NER

Ruotian Ma, Yiding Tan, Xin Zhou +6

Input distribution shift is one of the vital problems in unsupervised domain adaptation (UDA). The most popular UDA approaches focus on domain-invariant representation learning, tr…

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