activity
20182022
most citedTriple-to-Text: Converting RDF Triples into High-Quality Natural Languages via Optimizing an Inverse KL Divergence

17 citations · 41 across the 5 of their papers we have counts for

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9 papers · 1 filter

cs.CL2022★ 1 cited

Beyond Triplet: Leveraging the Most Data for Multimodal Machine Translation

Yaoming Zhu, Zewei Sun, Shanbo Cheng +3

Multimodal machine translation (MMT) aims to improve translation quality by incorporating information from other modalities, such as vision. Previous MMT systems mainly focus on be…

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.CL2021★ 6 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.CL2021

Learning When to Translate for Streaming Speech

Qianqian Dong, Yaoming Zhu, Mingxuan Wang +1

How to find proper moments to generate partial sentence translation given a streaming speech input? Existing approaches waiting-and-translating for a fixed duration often break the…

cs.CL2021

Counter-Interference Adapter for Multilingual Machine Translation

Yaoming Zhu, Jiangtao Feng, Chengqi Zhao +2

Developing a unified multilingual model has long been a pursuit for machine translation. However, existing approaches suffer from performance degradation -- a single multilingual m…

cs.CL2020

The Volctrans Machine Translation System for WMT20

Liwei Wu, Xiao Pan, Zehui Lin +3

This paper describes our VolcTrans system on WMT20 shared news translation task. We participated in 8 translation directions. Our basic systems are based on Transformer, with sever…