Search Results for author: Xuancheng Huang

Found 7 papers, 4 papers with code

An Extensible Plug-and-Play Method for Multi-Aspect Controllable Text Generation

1 code implementation19 Dec 2022 Xuancheng Huang, Zijun Liu, Peng Li, Tao Li, Maosong Sun, Yang Liu

Recently, multi-aspect controllable text generation that controls the generated text in multiple aspects (e. g., sentiment, topic, and keywords) has attracted increasing attention.

Machine Translation Text Generation +1

Transfer Learning for Sequence Generation: from Single-source to Multi-source

1 code implementation ACL 2021 Xuancheng Huang, Jingfang Xu, Maosong Sun, Yang Liu

Although directly finetuning pretrained models on MSG tasks and concatenating multiple sources into a single long sequence is regarded as a simple method to transfer pretrained models to MSG tasks, we conjecture that the direct finetuning method leads to catastrophic forgetting and solely relying on pretrained self-attention layers to capture cross-source information is not sufficient.

Automatic Post-Editing Document Summarization +3

Neural Machine Translation: A Review of Methods, Resources, and Tools

no code implementations31 Dec 2020 Zhixing Tan, Shuo Wang, Zonghan Yang, Gang Chen, Xuancheng Huang, Maosong Sun, Yang Liu

Machine translation (MT) is an important sub-field of natural language processing that aims to translate natural languages using computers.

Data Augmentation Machine Translation +2

Modeling Voting for System Combination in Machine Translation

1 code implementation14 Jul 2020 Xuancheng Huang, Jiacheng Zhang, Zhixing Tan, Derek F. Wong, Huanbo Luan, Jingfang Xu, Maosong Sun, Yang Liu

System combination is an important technique for combining the hypotheses of different machine translation systems to improve translation performance.

Machine Translation Translation

Learning to Copy for Automatic Post-Editing

2 code implementations IJCNLP 2019 Xuancheng Huang, Yang Liu, Huanbo Luan, Jingfang Xu, Maosong Sun

To better identify translation errors, our method learns the representations of source sentences and system outputs in an interactive way.

Automatic Post-Editing Translation

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