Papers by Chulun Zhou

Confidence Based Bidirectional Global Context Aware Training Framework for Neural Machine Translation (2022.acl-long)

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Chulun Zhou, Fandong Meng, Jie Zhou, Min Zhang, Hongji Wang, Jinsong Su

Challenge:	Existing studies focus on how to effectively exploit bidirectional global contexts in neural machine translation models.
Approach:	They propose a Confidence Based Bidirectional Global Context Aware training framework for NMT . they incorporate bidirectional global context to the NMT model on unconfidently-predicted target words .
Outcome:	The proposed framework improves the NMT model on three large-scale translation datasets by +1.02, +0.57 BLEU scores.

Towards Making the Most of Dialogue Characteristics for Neural Chat Translation (2021.emnlp-main)

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Yunlong Liang, Chulun Zhou, Fandong Meng, Jinan Xu, Yufeng Chen, Jinsong Su, Jie Zhou

Multilinguality

Challenge:	Neural Chat Translation (NCT) models that use dialogue characteristics of chat are often incoherent and speakerirrelevant.
Approach:	They propose to introduce the modeling of dialogue characteristics into the NCT model by capturing the inherent dialogue characteristics.
Outcome:	The proposed model can translate conversational text between speakers of different languages.

Towards Robust Neural Machine Translation with Iterative Scheduled Data-Switch Training (2022.coling-1)

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Zhongjian Miao, Xiang Li, Liyan Kang, Wen Zhang, Chulun Zhou, Yidong Chen, Bin Wang, Min Zhang, Jinsong Su

Robustness in NLP

Challenge:	Existing methods on robust neural machine translation (NMT) construct adversarial examples by injecting noise into authentic examples and indiscriminately exploit two types of examples.
Approach:	They propose an iterative scheduled data-switch training framework to mitigate this problem by injecting noise into authentic examples and indiscriminately exploiting two types of examples.
Outcome:	The proposed model outperforms several competitive benchmarks on four translation benchmarks.

A Variational Hierarchical Model for Neural Cross-Lingual Summarization (2022.acl-long)

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Yunlong Liang, Fandong Meng, Chulun Zhou, Jinan Xu, Yufeng Chen, Jinsong Su, Jie Zhou

Text Generation

Challenge:	Existing studies on cross-lingual summarization focus on pipeline methods or jointly training an end-to-end model through an auxiliary MT or MS objective.
Approach:	They propose a hierarchical model for the cross-lingual summarization task . the model is based on the conditional variational auto-encoder .
Outcome:	The proposed model generates better cross-lingual summaries than comparison models in the few-shot setting.

The Essence of Contextual Understanding in Theory of Mind: A Study on Question Answering with Story Characters (2025.acl-long)

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Chulun Zhou, Qiujing Wang, Mo Yu, Xiaoqian Yue, Rui Lu, Jiangnan Li, Yifan Zhou, Shunchi Zhang, Jie Zhou, Wai Lam

Natural Language Interfaces

Challenge:	Theory-of-Mind (ToM) is a psychological capability that allows humans to understand and interpret the mental states of others.
Approach:	They propose a CharToM-QA benchmark to assess the importance of comprehensive contextual understanding about personal backgrounds in ToM.
Outcome:	The proposed model outperforms existing models on 1,035 ToM questions based on classic novels and shows that educated participants perform better when they have read the novels than non-educated participants.

A Novel Graph-based Multi-modal Fusion Encoder for Neural Machine Translation (2020.acl-main)

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Yongjing Yin, Fandong Meng, Jinsong Su, Chulun Zhou, Zhengyuan Yang, Jie Zhou, Jiebo Luo

Multimodality

Challenge:	Existing multi-modal neural machine translation models do not fully exploit fine-grained semantic correspondences between semantic units of different modalities.
Approach:	They propose a graph-based multi-modal fusion encoder that exploits fine-grained semantic correspondences between different modalities.
Outcome:	The proposed encoder significantly extends the conventional text-based translation by taking images as additional inputs.

Getting the Most out of Simile Recognition (2022.findings-emnlp)

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Xiaoyue Wang, Linfeng Song, Xin Liu, Chulun Zhou, Hualin Zeng, Jinsong Su

Challenge:	Recent work ignores features other than surface strings and suffers from data hunger issue.
Approach:	They propose to use simile sentence classification and simile component extraction to find simile components.
Outcome:	The proposed model outperforms current state-of-the-art systems and baselines.

HyperNetwork-based Decoupling to Improve Model Generalization for Few-Shot Relation Extraction (2023.emnlp-main)

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Liang Zhang, Chulun Zhou, Fandong Meng, Jinsong Su, Yidong Chen, Jie Zhou

Information Extraction & Text Mining

Challenge:	Existing studies cannot generalize well to unseen relations using Prototypical Networks . current approaches are dependent on large amount of labeled data and cannot deal with unseense relations well.
Approach:	They propose a HyperNetwork-based Decoupling approach to improve FSRE generalization . they propose FSre models with an encoder, network generator and refined classifiers .
Outcome:	The proposed method improves the generalization of few-shot relation extraction models.

Exploring Dynamic Selection of Branch Expansion Orders for Code Generation (2021.acl-long)

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Hui Jiang, Chulun Zhou, Fandong Meng, Biao Zhang, Jie Zhou, Degen Huang, Qingqiang Wu, Jinsong Su

Programming Languages in NLP

Challenge:	Existing code generation models model abstract syntax tree (AST) but not suitable for all multi-branch nodes.
Approach:	They propose to equip a Seq2Tree model with a branch selector to determine optimal expansion orders for multi-branch nodes.
Outcome:	The proposed model can determine optimal expansion orders of branches for multi-branch nodes.

Towards Robust k-Nearest-Neighbor Machine Translation (2022.emnlp-main)

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Hui Jiang, Ziyao Lu, Fandong Meng, Chulun Zhou, Jie Zhou, Degen Huang, Jinsong Su

Text Generation

Challenge:	k-Nearest-Neighbor Machine Translation (kNN-MT) is a popular research paradigm in machine translation.
Approach:	They propose a confidence-enhanced kNN-MT model with robust training to reduce noise . they introduce NMT confidence to refine the modeling of important components of kN-MT .
Outcome:	The proposed model improves on four benchmark datasets and is robust to training.

RC3: Regularized Contrastive Cross-lingual Cross-modal Pre-training (2023.findings-acl)

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Chulun Zhou, Yunlong Liang, Fandong Meng, Jinan Xu, Jinsong Su, Jie Zhou

Multilinguality

Challenge:	Existing V&L pre-training methods rely on strictly-aligned multilingual image-text pairs generated from English-centric datasets.
Approach:	They propose a regularized cross-lingual visual contrastive learning objective that constrains representation proximity of weakly-aligned multilingual image-text pairs.
Outcome:	The proposed model outperforms competing models with weak zero-shot capability on 5 multi-modal tasks across 6 languages.

Exploring Contextual Word-level Style Relevance for Unsupervised Style Transfer (2020.acl-main)

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Chulun Zhou, Liangyu Chen, Jiachen Liu, Xinyan Xiao, Jinsong Su, Sheng Guo, Hua Wu

Low-Resource NLP

Challenge:	Existing methods to unsupervised style transfer lack fine-grained control of the influence from the target style.
Approach:	They propose a model that exploits the relevance of each output word to the target style . they pretrain a style classifier and train an attentional Seq2seq model to reconstruct input sentences .
Outcome:	The proposed model achieves state-of-the-art performance in terms of transfer accuracy and content preservation.