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Boxing Chen

Alibaba Group

10 papers hereh-index 241.6k citations70 works total

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

author position
  • middle author8
  • last author2

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

fields
  • cs.CL9
  • cs.SE1
affiliations
  • Alibaba Group
Homepage
same name
  • Boxing Chen — 25 papers, h 18
  • Boxing Chen — 20 papers, h 7
  • Boxing Chen — 10 papers, h 4
  • Boxing Chen — 8 papers, h 4
  • Boxing Chen — 4 papers, h 2
  • Boxing Chen — 4 papers, 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

activity
20192023
most citedSelf-Paced Learning for Neural Machine Translation

39 citations · 70 across the 10 of their papers we have counts for

collaborators
Showing 2019Show all

4 papers · 1 filter

cs.CL2019★ 1 cited

Visual Agreement Regularized Training for Multi-Modal Machine Translation

Pengcheng Yang, Boxing Chen, Pei Zhang +1

Multi-modal machine translation aims at translating the source sentence into a different language in the presence of the paired image. Previous work suggests that additional visual…

cs.CL2019★ 8 cited

Cross-lingual Pre-training Based Transfer for Zero-shot Neural Machine Translation

Baijun Ji, Zhirui Zhang, Xiangyu Duan +3

Transfer learning between different language pairs has shown its effectiveness for Neural Machine Translation (NMT) in low-resource scenario. However, existing transfer methods inv…

cs.CL2019★ 3 cited

Neural Zero-Inflated Quality Estimation Model For Automatic Speech Recognition System

Kai Fan, Jiayi Wang, Bo Li +4

The performances of automatic speech recognition (ASR) systems are usually evaluated by the metric word error rate (WER) when the manually transcribed data are provided, which are,…

cs.CL2019★ 8 cited

Lattice Transformer for Speech Translation

Pei Zhang, Boxing Chen, Niyu Ge +1

Recent advances in sequence modeling have highlighted the strengths of the transformer architecture, especially in achieving state-of-the-art machine translation results. However,…

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