Yiren Wang


2023

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Leveraging GPT-4 for Automatic Translation Post-Editing
Vikas Raunak | Amr Sharaf | Yiren Wang | Hany Awadalla | Arul Menezes
Findings of the Association for Computational Linguistics: EMNLP 2023

While Neural Machine Translation (NMT) represents the leading approach to Machine Translation (MT), the outputs of NMT models still require translation post-editing to rectify errors and enhance quality under critical settings. In this work, we formalize the task of direct translation post-editing with Large Language Models (LLMs) and explore the use of GPT-4 to automatically post-edit NMT outputs across several language pairs. Our results demonstrate that GPT-4 is adept at translation post-editing, producing meaningful and trustworthy edits to translations that help improve its general quality as well as remove different classes of major errors in translations. In particular, human evaluations on assessing edit trustworthiness show that GPT-4 exhibits a large improvement over the prior state-of-the-art LLM. Notably, we improve upon state-of-the-art performance on WMT-22 English-Chinese, English-German, Chinese-English and German-English language pairs using GPT-4 based post-editing, as evaluated by state-of-the-art MT quality metrics. However, we also show that GPT-4 could produce hallucinated edits, thereby urging caution in its use as an expert translation post-editor.

2020

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Multi-task Learning for Multilingual Neural Machine Translation
Yiren Wang | ChengXiang Zhai | Hany Hassan
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

While monolingual data has been shown to be useful in improving bilingual neural machine translation (NMT), effectively and efficiently leveraging monolingual data for Multilingual NMT (MNMT) systems is a less explored area. In this work, we propose a multi-task learning (MTL) framework that jointly trains the model with the translation task on bitext data and two denoising tasks on the monolingual data. We conduct extensive empirical studies on MNMT systems with 10 language pairs from WMT datasets. We show that the proposed approach can effectively improve the translation quality for both high-resource and low-resource languages with large margin, achieving significantly better results than the individual bilingual models. We also demonstrate the efficacy of the proposed approach in the zero-shot setup for language pairs without bitext training data. Furthermore, we show the effectiveness of MTL over pre-training approaches for both NMT and cross-lingual transfer learning NLU tasks; the proposed approach outperforms massive scale models trained on single task.

2019

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Depth Growing for Neural Machine Translation
Lijun Wu | Yiren Wang | Yingce Xia | Fei Tian | Fei Gao | Tao Qin | Jianhuang Lai | Tie-Yan Liu
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

While very deep neural networks have shown effectiveness for computer vision and text classification applications, how to increase the network depth of the neural machine translation (NMT) models for better translation quality remains a challenging problem. Directly stacking more blocks to the NMT model results in no improvement and even drop in performance. In this work, we propose an effective two-stage approach with three specially designed components to construct deeper NMT models, which result in significant improvements over the strong Transformer baselines on WMT14 EnglishGerman and EnglishFrench translation tasks.

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Exploiting Monolingual Data at Scale for Neural Machine Translation
Lijun Wu | Yiren Wang | Yingce Xia | Tao Qin | Jianhuang Lai | Tie-Yan Liu
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

While target-side monolingual data has been proven to be very useful to improve neural machine translation (briefly, NMT) through back translation, source-side monolingual data is not well investigated. In this work, we study how to use both the source-side and target-side monolingual data for NMT, and propose an effective strategy leveraging both of them. First, we generate synthetic bitext by translating monolingual data from the two domains into the other domain using the models pretrained on genuine bitext. Next, a model is trained on a noised version of the concatenated synthetic bitext where each source sequence is randomly corrupted. Finally, the model is fine-tuned on the genuine bitext and a clean version of a subset of the synthetic bitext without adding any noise. Our approach achieves state-of-the-art results on WMT16, WMT17, WMT18 EnglishGerman translations and WMT19 GermanFrench translations, which demonstrate the effectiveness of our method. We also conduct a comprehensive study on how each part in the pipeline works.

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Microsoft Research Asia’s Systems for WMT19
Yingce Xia | Xu Tan | Fei Tian | Fei Gao | Di He | Weicong Chen | Yang Fan | Linyuan Gong | Yichong Leng | Renqian Luo | Yiren Wang | Lijun Wu | Jinhua Zhu | Tao Qin | Tie-Yan Liu
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

We Microsoft Research Asia made submissions to 11 language directions in the WMT19 news translation tasks. We won the first place for 8 of the 11 directions and the second place for the other three. Our basic systems are built on Transformer, back translation and knowledge distillation. We integrate several of our rececent techniques to enhance the baseline systems: multi-agent dual learning (MADL), masked sequence-to-sequence pre-training (MASS), neural architecture optimization (NAO), and soft contextual data augmentation (SCA).

2016

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Recurrent Residual Learning for Sequence Classification
Yiren Wang | Fei Tian
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing