Pengzhi Gao


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Bi-SimCut: A Simple Strategy for Boosting Neural Machine Translation
Pengzhi Gao | Zhongjun He | Hua Wu | Haifeng Wang
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

We introduce Bi-SimCut: a simple but effective training strategy to boost neural machine translation (NMT) performance. It consists of two procedures: bidirectional pretraining and unidirectional finetuning. Both procedures utilize SimCut, a simple regularization method that forces the consistency between the output distributions of the original and the cutoff sentence pairs. Without leveraging extra dataset via back-translation or integrating large-scale pretrained model, Bi-SimCut achieves strong translation performance across five translation benchmarks (data sizes range from 160K to 20.2M): BLEU scores of 31.16 for ende and 38.37 for deen on the IWSLT14 dataset, 30.78 for ende and 35.15 for deen on the WMT14 dataset, and 27.17 for zhen on the WMT17 dataset. SimCut is not a new method, but a version of Cutoff (Shen et al., 2020) simplified and adapted for NMT, and it could be considered as a perturbation-based method. Given the universality and simplicity of Bi-SimCut and SimCut, we believe they can serve as strong baselines for future NMT research.


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Mixup Decoding for Diverse Machine Translation
Jicheng Li | Pengzhi Gao | Xuanfu Wu | Yang Feng | Zhongjun He | Hua Wu | Haifeng Wang
Findings of the Association for Computational Linguistics: EMNLP 2021

Diverse machine translation aims at generating various target language translations for a given source language sentence. To leverage the linear relationship in the sentence latent space introduced by the mixup training, we propose a novel method, MixDiversity, to generate different translations for the input sentence by linearly interpolating it with different sentence pairs sampled from the training corpus during decoding. To further improve the faithfulness and diversity of the translations, we propose two simple but effective approaches to select diverse sentence pairs in the training corpus and adjust the interpolation weight for each pair correspondingly. Moreover, by controlling the interpolation weight, our method can achieve the trade-off between faithfulness and diversity without any additional training, which is required in most of the previous methods. Experiments on WMT’16 en-ro, WMT’14 en-de, and WMT’17 zh-en are conducted to show that our method substantially outperforms all previous diverse machine translation methods.


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A Data-Centric Framework for Composable NLP Workflows
Zhengzhong Liu | Guanxiong Ding | Avinash Bukkittu | Mansi Gupta | Pengzhi Gao | Atif Ahmed | Shikun Zhang | Xin Gao | Swapnil Singhavi | Linwei Li | Wei Wei | Zecong Hu | Haoran Shi | Xiaodan Liang | Teruko Mitamura | Eric Xing | Zhiting Hu
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Empirical natural language processing (NLP) systems in application domains (e.g., healthcare, finance, education) involve interoperation among multiple components, ranging from data ingestion, human annotation, to text retrieval, analysis, generation, and visualization. We establish a unified open-source framework to support fast development of such sophisticated NLP workflows in a composable manner. The framework introduces a uniform data representation to encode heterogeneous results by a wide range of NLP tasks. It offers a large repository of processors for NLP tasks, visualization, and annotation, which can be easily assembled with full interoperability under the unified representation. The highly extensible framework allows plugging in custom processors from external off-the-shelf NLP and deep learning libraries. The whole framework is delivered through two modularized yet integratable open-source projects, namely Forte (for workflow infrastructure and NLP function processors) and Stave (for user interaction, visualization, and annotation).