1 code implementation • ACL 2022 • Minghuan Tan, Yong Dai, Duyu Tang, Zhangyin Feng, Guoping Huang, Jing Jiang, Jiwei Li, Shuming Shi
We find that a frozen GPT achieves state-of-the-art performance on perfect pinyin.
1 code implementation • NAACL 2022 • Jiahao Xu, Yubin Ruan, Wei Bi, Guoping Huang, Shuming Shi, Lihui Chen, Lemao Liu
Back translation (BT) is one of the most significant technologies in NMT research fields.
1 code implementation • 23 Oct 2023 • Xingyu Chen, Lemao Liu, Guoping Huang, Zhirui Zhang, Mingming Yang, Shuming Shi, Rui Wang
Word-Level Auto-Completion (WLAC) plays a crucial role in Computer-Assisted Translation.
no code implementations • 1 Nov 2018 • Long Zhou, Yuchen Liu, Jiajun Zhang, Cheng-qing Zong, Guoping Huang
Current Neural Machine Translation (NMT) employs a language-specific encoder to represent the source sentence and adopts a language-specific decoder to generate target translation.
no code implementations • WS 2017 • Guoping Huang, Jiajun Zhang, Yu Zhou, Cheng-qing Zong
Terms extensively exist in specific domains, and term translation plays a critical role in domain-specific machine translation (MT) tasks.
no code implementations • 13 Aug 2019 • Huayang Li, Guoping Huang, Deng Cai, Lemao Liu
Experiments show that our approach can indeed improve the translation quality with the automatically generated constraints.
no code implementations • ACL 2020 • Xintong Li, Lemao Liu, Rui Wang, Guoping Huang, Max Meng
This paper first provides a method to identify source and target contexts and then introduce a gate mechanism to control the source and target contributions in Transformer.
no code implementations • IJCNLP 2019 • Guanlin Li, Lemao Liu, Guoping Huang, Conghui Zhu, Tiejun Zhao
Many Data Augmentation (DA) methods have been proposed for neural machine translation.
no code implementations • ACL 2020 • Jierui Li, Lemao Liu, Huayang Li, Guanlin Li, Guoping Huang, Shuming Shi
Recently many efforts have been devoted to interpreting the black-box NMT models, but little progress has been made on metrics to evaluate explanation methods.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Huayang Li, Lemao Liu, Guoping Huang, Shuming Shi
Many efforts have been devoted to extracting constituency trees from pre-trained language models, often proceeding in two stages: feature definition and parsing.
no code implementations • 15 May 2021 • Qu Cui, ShuJian Huang, Jiahuan Li, Xiang Geng, Zaixiang Zheng, Guoping Huang, Jiajun Chen
However, we argue that there are gaps between the predictor and the estimator in both data quality and training objectives, which preclude QE models from benefiting from a large number of parallel corpora more directly.
no code implementations • 27 May 2021 • Guoping Huang, Lemao Liu, Xing Wang, Longyue Wang, Huayang Li, Zhaopeng Tu, Chengyan Huang, Shuming Shi
Automatic machine translation is super efficient to produce translations yet their quality is not guaranteed.
no code implementations • ACL 2021 • Huayang Li, Lemao Liu, Guoping Huang, Shuming Shi
In this paper, we propose the task of general word-level autocompletion (GWLAN) from a real-world CAT scenario, and construct the first public benchmark to facilitate research in this topic.
no code implementations • Asian Chapter of the Association for Computational Linguistics 2020 • Qian Wang, Jiajun Zhang, Lemao Liu, Guoping Huang, Chengqing Zong
We propose a touch-based editing method for translation, which is more flexible than traditional keyboard-mouse-based translation postediting.
no code implementations • ACL 2021 • Qiuxiang He, Guoping Huang, Qu Cui, Li Li, Lemao Liu
It is generally believed that a translation memory (TM) should be beneficial for machine translation tasks.
no code implementations • Findings (ACL) 2022 • Jiannan Xiang, Huayang Li, Defu Lian, Guoping Huang, Taro Watanabe, Lemao Liu
To this end, we study the dynamic relationship between the encoded linguistic information and task performance from the viewpoint of Pareto Optimality.
no code implementations • Findings (ACL) 2022 • Jiannan Xiang, Huayang Li, Yahui Liu, Lemao Liu, Guoping Huang, Defu Lian, Shuming Shi
Current practices in metric evaluation focus on one single dataset, e. g., Newstest dataset in each year's WMT Metrics Shared Task.
no code implementations • ACL 2022 • Yanling Xiao, Lemao Liu, Guoping Huang, Qu Cui, ShuJian Huang, Shuming Shi, Jiajun Chen
In this work, we propose a novel BiTIIMT system, Bilingual Text-Infilling for Interactive Neural Machine Translation.
no code implementations • 3 Aug 2022 • Shuming Shi, Enbo Zhao, Duyu Tang, Yan Wang, Piji Li, Wei Bi, Haiyun Jiang, Guoping Huang, Leyang Cui, Xinting Huang, Cong Zhou, Yong Dai, Dongyang Ma
In Effidit, we significantly expand the capacities of a writing assistant by providing functions in five categories: text completion, error checking, text polishing, keywords to sentences (K2S), and cloud input methods (cloud IME).
no code implementations • 12 Jun 2023 • Hongkun Hao, Guoping Huang, Lemao Liu, Zhirui Zhang, Shuming Shi, Rui Wang
The finding demonstrates that TM-augmented NMT is good at the ability of fitting data (i. e., lower bias) but is more sensitive to the fluctuations in the training data (i. e., higher variance), which provides an explanation to a recently reported contradictory phenomenon on the same translation task: TM-augmented NMT substantially advances vanilla NMT under the high-resource scenario whereas it fails under the low-resource scenario.