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20212024
most citedSelf-Distillation Mixup Training for Non-autoregressive Neural Machine Translation

8 citations · 14 across the 5 of their papers we have counts for

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5 papers

cs.CL20241 cited

SemEval-2024 Task 8: Multidomain, Multimodel and Multilingual Machine-Generated Text Detection

Yuxia Wang, Jonibek Mansurov, Petar Ivanov +12

We present the results and the main findings of SemEval-2024 Task 8: Multigenerator, Multidomain, and Multilingual Machine-Generated Text Detection. The task featured three subtask…

cs.CL2023

Collective Human Opinions in Semantic Textual Similarity

Yuxia Wang, Shimin Tao, Ning Xie +3

Despite the subjective nature of semantic textual similarity (STS) and pervasive disagreements in STS annotation, existing benchmarks have used averaged human ratings as the gold s…

cs.CL20215 cited

Diformer: Directional Transformer for Neural Machine Translation

Minghan Wang, Jiaxin Guo, Yuxia Wang +8

Autoregressive (AR) and Non-autoregressive (NAR) models have their own superiority on the performance and latency, combining them into one model may take advantage of both. Current…

cs.CL2021

Joint-training on Symbiosis Networks for Deep Nueral Machine Translation models

Zhengzhe Yu, Jiaxin Guo, Minghan Wang +11

Deep encoders have been proven to be effective in improving neural machine translation (NMT) systems, but it reaches the upper bound of translation quality when the number of encod…

cs.CL20218 cited

Self-Distillation Mixup Training for Non-autoregressive Neural Machine Translation

Jiaxin Guo, Minghan Wang, Daimeng Wei +11

Recently, non-autoregressive (NAT) models predict outputs in parallel, achieving substantial improvements in generation speed compared to autoregressive (AT) models. While performi…