activity
20152022
most citedUniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training

225 citations · 950 across the 56 of their papers we have counts for

collaborators

97 papers

cs.CL202213 cited

Momentum Calibration for Text Generation

Xingxing Zhang, Yiran Liu, Xun Wang +5

The input and output of most text generation tasks can be transformed to two sequences of tokens and they can be modeled using sequence-to-sequence learning modeling tools such as…

cs.CL20222 cited

LVP-M3: Language-aware Visual Prompt for Multilingual Multimodal Machine Translation

Hongcheng Guo, Jiaheng Liu, Haoyang Huang +5

Multimodal Machine Translation (MMT) focuses on enhancing text-only translation with visual features, which has attracted considerable attention from both natural language processi…

cs.LG20226 cited

TorchScale: Transformers at Scale

Shuming Ma, Hongyu Wang, Shaohan Huang +8

Large Transformers have achieved state-of-the-art performance across many tasks. Most open-source libraries on scaling Transformers focus on improving training or inference with be…

cs.SD20222 cited

Joint Pre-Training with Speech and Bilingual Text for Direct Speech to Speech Translation

Kun Wei, Long Zhou, Ziqiang Zhang +5

Direct speech-to-speech translation (S2ST) is an attractive research topic with many advantages compared to cascaded S2ST. However, direct S2ST suffers from the data scarcity probl…

cs.CL20223 cited

Beyond English-Centric Bitexts for Better Multilingual Language Representation Learning

Barun Patra, Saksham Singhal, Shaohan Huang +5

In this paper, we elaborate upon recipes for building multilingual representation models that are not only competitive with existing state-of-the-art models but are also more param…

cs.CV202214 cited

A Unified View of Masked Image Modeling

Zhiliang Peng, Li Dong, Hangbo Bao +2

Masked image modeling has demonstrated great potential to eliminate the label-hungry problem of training large-scale vision Transformers, achieving impressive performance on variou…