activity
20182022
most citedAcquiring Knowledge from Pre-trained Model to Neural Machine Translation

11 citations · 29 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2022

Unified Multimodal Punctuation Restoration Framework for Mixed-Modality Corpus

Yaoming Zhu, Liwei Wu, Shanbo Cheng +1

The punctuation restoration task aims to correctly punctuate the output transcriptions of automatic speech recognition systems. Previous punctuation models, either using text only…

cs.CL20216 cited

The Volctrans GLAT System: Non-autoregressive Translation Meets WMT21

Lihua Qian, Yi Zhou, Zaixiang Zheng +7

This paper describes the Volctrans' submission to the WMT21 news translation shared task for German->English translation. We build a parallel (i.e., non-autoregressive) translation…

cs.CL20212 cited

Language Tags Matter for Zero-Shot Neural Machine Translation

Liwei Wu, Shanbo Cheng, Mingxuan Wang +1

Multilingual Neural Machine Translation (MNMT) has aroused widespread interest due to its efficiency. An exciting advantage of MNMT models is that they could also translate between…

cs.CL20204 cited

AR: Auto-Repair the Synthetic Data for Neural Machine Translation

Shanbo Cheng, Shaohui Kuang, Rongxiang Weng +3

Compared with only using limited authentic parallel data as training corpus, many studies have proved that incorporating synthetic parallel data, which generated by back translatio…

cs.CL201911 cited

Acquiring Knowledge from Pre-trained Model to Neural Machine Translation

Rongxiang Weng, Heng Yu, Shujian Huang +2

Pre-training and fine-tuning have achieved great success in the natural language process field. The standard paradigm of exploiting them includes two steps: first, pre-training a m…

cs.CL2018

Improving Multilingual Semantic Textual Similarity with Shared Sentence Encoder for Low-resource Languages

Xin Tang, Shanbo Cheng, Loc Do +5

Measuring the semantic similarity between two sentences (or Semantic Textual Similarity - STS) is fundamental in many NLP applications. Despite the remarkable results in supervised…