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researcher

Yen-Chun Chen

Microsoft

8 papers hereh-index 167.3k citations19 works total

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

author position
  • first author2
  • middle author5

Across the 7 of 8 papers where every author was matched, so the position is known.

fields
  • cs.CL4
  • cs.CV4
affiliations
  • Microsoft
same name
  • Yen-Chun Chen — 9 papers, h 9
  • Yen-Chun Chen — 4 papers, h 8
  • Yen-Chun Chen — 3 papers, h 2
  • Yen-Chun Chen — 2 papers
  • Yen-Chun Chen — 1 paper, h 6
  • Yen-Chun Chen — 1 paper, h 14

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
20182022
most citedHERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training

51 citations · 82 across the 4 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2019

Distilling Knowledge Learned in BERT for Text Generation

Yen-Chun Chen, Zhe Gan, Yu Cheng +2

Large-scale pre-trained language model such as BERT has achieved great success in language understanding tasks. However, it remains an open question how to utilize BERT for languag…

cs.CL2019

DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation

Yizhe Zhang, Siqi Sun, Michel Galley +6

We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained transformer). Trained on 147M conversation-like exchanges ext…

cs.CL2019★ 9 cited

Explore, Propose, and Assemble: An Interpretable Model for Multi-Hop Reading Comprehension

Yichen Jiang, Nitish Joshi, Yen-Chun Chen +1

Multi-hop reading comprehension requires the model to explore and connect relevant information from multiple sentences/documents in order to answer the question about the context.…

cs.CL2018

Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting

Yen-Chun Chen, Mohit Bansal

Inspired by how humans summarize long documents, we propose an accurate and fast summarization model that first selects salient sentences and then rewrites them abstractively (i.e.…

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