Nini Xiao


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Disambiguated Lexically Constrained Neural Machine Translation
Jinpeng Zhang | Nini Xiao | Ke Wang | Chuanqi Dong | Xiangyu Duan | Yuqi Zhang | Min Zhang
Findings of the Association for Computational Linguistics: ACL 2023

Lexically constrained neural machine translation (LCNMT), which controls the translation generation with pre-specified constraints, is important in many practical applications. Current approaches to LCNMT typically assume that the pre-specified lexicon constraints are contextually appropriate. This assumption limits their application to real-world scenarios where a source lexicon may have multiple target constraints, and disambiguation is needed to select the most suitable one. In this paper, we propose disambiguated LCNMT (D-LCNMT) to solve the problem. D-LCNMT is a robust and effective two-stage framework that disambiguates the constraints based on contexts at first, then integrates the disambiguated constraints into LCNMT. Experimental results show that our approach outperforms strong baselines including existing data argumentation based approaches on benchmark datasets, and comprehensive experiments in scenarios where a source lexicon corresponds to multiple target constraints demonstrate the constraint disambiguation superiority of our approach.


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TSMind: Alibaba and Soochow University’s Submission to the WMT22 Translation Suggestion Task
Xin Ge | Ke Wang | Jiayi Wang | Nini Xiao | Xiangyu Duan | Yu Zhao | Yuqi Zhang
Proceedings of the Seventh Conference on Machine Translation (WMT)

This paper describes the joint submission of Alibaba and Soochow University to the WMT 2022 Shared Task on Translation Suggestion (TS). We participate in the English to/from German and English to/from Chinese tasks. Basically, we utilize the model paradigm fine-tuning on the downstream tasks based on large-scale pre-trained models, which has recently achieved great success. We choose FAIR’s WMT19 English to/from German news translation system and MBART50 for English to/from Chinese as our pre-trained models. Considering the task’s condition of limited use of training data, we follow the data augmentation strategies provided by Yang to boost our TS model performance. And we further involve the dual conditional cross-entropy model and GPT-2 language model to filter augmented data. The leader board finally shows that our submissions are ranked first in three of four language directions in the Naive TS task of the WMT22 Translation Suggestion task.


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Combining Static Word Embeddings and Contextual Representations for Bilingual Lexicon Induction
Jinpeng Zhang | Baijun Ji | Nini Xiao | Xiangyu Duan | Min Zhang | Yangbin Shi | Weihua Luo
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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基于层间知识蒸馏的神经机器翻译(Inter-layer Knowledge Distillation for Neural Machine Translation)
Chang Jin (金畅) | Renchong Duan (段仁翀) | Nini Xiao (肖妮妮) | Xiangyu Duan (段湘煜)
Proceedings of the 20th Chinese National Conference on Computational Linguistics