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20192022
most citedIterative Domain-Repaired Back-Translation

2 citations · 3 across the 4 of their papers we have counts for

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5 papers · 1 filter

cs.CL2022

Competency-Aware Neural Machine Translation: Can Machine Translation Know its Own Translation Quality?

Pei Zhang, Baosong Yang, Haoran Wei +4

Neural machine translation (NMT) is often criticized for failures that happen without awareness. The lack of competency awareness makes NMT untrustworthy. This is in sharp contrast…

cs.CL2020

Incorporating BERT into Parallel Sequence Decoding with Adapters

Junliang Guo, Zhirui Zhang, Linli Xu +3

While large scale pre-trained language models such as BERT have achieved great success on various natural language understanding tasks, how to efficiently and effectively incorpora…

cs.CL20202 cited

Iterative Domain-Repaired Back-Translation

Hao-Ran Wei, Zhirui Zhang, Boxing Chen +1

In this paper, we focus on the domain-specific translation with low resources, where in-domain parallel corpora are scarce or nonexistent. One common and effective strategy for thi…

cs.CL2020

GRET: Global Representation Enhanced Transformer

Rongxiang Weng, Haoran Wei, Shujian Huang +4

Transformer, based on the encoder-decoder framework, has achieved state-of-the-art performance on several natural language generation tasks. The encoder maps the words in the input…

cs.CL20191 cited

Generating Diverse Translation by Manipulating Multi-Head Attention

Zewei Sun, Shujian Huang, Hao-Ran Wei +2

Transformer model has been widely used on machine translation tasks and obtained state-of-the-art results. In this paper, we report an interesting phenomenon in its encoder-decoder…