Papers by Shuming Shi

Topic-Aware Neural Keyphrase Generation for Social Media Language (P19-1)

Challenge:	Existing evaluation metrics for open-domain dialogue systems are limited by the diversity of possible outcomings.
Approach:	They propose a method to augment a reference set to improve reliability . they propose BLEU to measure similarity between a predicted response and a small set of references .
Outcome:	The proposed model improves the reliability of reference-based metrics with augmented reference sets.

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Yue Wang, Jing Li, Hou Pong Chan, Irwin King, Michael R. Lyu, Shuming Shi

A Survey on Zero Pronoun Translation (2023.acl-long)

Challenge:	Existing methods to extract words from source posts to form keyphrases do not exploit latent topics.
Approach:	They propose a sequence-to-sequence-based neural keyphrase generation framework . it allows absent keyphrases to be created, and it allows joint modeling of latent topic representations .
Outcome:	The proposed model outperforms extraction and generation models without exploiting latent topics.

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Longyue Wang, Siyou Liu, Mingzhou Xu, Linfeng Song, Shuming Shi, Zhaopeng Tu

Knowledge Verification to Nip Hallucination in the Bud (2024.emnlp-main)

Challenge:	Zero pronouns (ZPs) are often omitted in pro-drop languages, but should be recalled in non-pro-drop language.
Approach:	They propose to analyze the literature on zero pronoun translation after the neural revolution . they uncover that data limitation causes learning bias in languages and domains .
Outcome:	The proposed method and methods are compared to other models and evaluation metrics on different benchmarks.

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Fanqi Wan, Xinting Huang, Leyang Cui, Xiaojun Quan, Wei Bi, Shuming Shi

Reasoning

Challenge:	Recent studies have shown that large language models generate responses that sound plausible but contradict factual knowledge, a phenomenon known as hallucination.
Approach:	They propose a novel approach to align large language models to evaluate knowledge boundaries based on external knowledge to reduce hallucinations .
Outcome:	The proposed approach reduces hallucinations across six benchmarks using foundation LLMs of varying backbones and scales.

Rethinking Negative Sampling for Handling Missing Entity Annotations (2022.acl-long)

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Yangming Li, Lemao Liu, Shuming Shi

Enhancing Grammatical Error Correction Systems with Explanations (2023.acl-long)

Challenge:	Empirical studies show low missampling rate and high uncertainty are both essential for achieving promising performances with negative sampling.
Approach:	They propose an adaptive and weighted sampling distribution that further improves negative sampling by introducing missampling and uncertainty concepts.
Outcome:	The proposed approach improves on synthetic and well-annotated datasets in terms of F1 score and loss convergence.

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Yuejiao Fei, Leyang Cui, Sen Yang, Wai Lam, Zhenzhong Lan, Shuming Shi

ParroT: Translating during Chat using Large Language Models tuned with Human Translation and Feedback (2023.findings-emnlp)

Challenge:	To help language learners better understand why the GEC system makes a correction, the causes of errors and the corresponding error types are two key factors.
Approach:	They propose to annotate large dataset with evidence words and grammatical error types to help language learners better understand corrections.
Outcome:	The proposed model can be validated by human evaluation and can be used to help second-language learners decide whether to accept a correction suggestion and understand the associated grammar rule.

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Wenxiang Jiao, Jen-tse Huang, Wenxuan Wang, Zhiwei He, Tian Liang, Xing Wang, Shuming Shi, Zhaopeng Tu

IMTLab: An Open-Source Platform for Building, Evaluating, and Diagnosing Interactive Machine Translation Systems (2023.emnlp-main)

Challenge:	Large language models (LLMs) like ChatGPT are only accessible through restricted APIs, which creates barriers to new research and advancements in the field.
Approach:	They propose a framework to enhance and regulate the translation abilities during chat . they reformulate translation data into the instruction-following style and introduce a "Hint" field .
Outcome:	The proposed framework enhances and regulates the translation abilities during chat . it reformulates translation data into the instruction-following style and introduces a "Hint" field .

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Xu Huang, Zhirui Zhang, Ruize Gao, Yichao Du, Lemao Liu, Guoping Huang, Shuming Shi, Jiajun Chen, Shujian Huang

hyperdoc2vec: Distributed Representations of Hypertext Documents (P18-1)

Challenge:	Existing systems that use a left-to-right completion paradigm are inefficient and expensive.
Approach:	They propose an open-source end-to-end interactive machine translation system platform . they propose to use a prefix-constrained decoding approach to achieve end- to-end evaluation .
Outcome:	The proposed system can guarantee high-quality, error-free translations . it uses prefix-constrained decoding and improves on previous systems .

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Jialong Han, Yan Song, Wayne Xin Zhao, Shuming Shi, Haisong Zhang

Representation Learning

Challenge:	Conventional text embedding methods suffer from information loss if directly adapted to hyper-documents.
Approach:	They propose an embedding approach for hyper-documents that incorporates four criteria to preserve necessary information for embeddable models.
Outcome:	The proposed model outperforms several existing models on two tasks in the academic domain.

Towards Better Modeling Hierarchical Structure for Self-Attention with Ordered Neurons (D19-1)

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Jie Hao, Xing Wang, Shuming Shi, Jinfeng Zhang, Zhaopeng Tu

Retrieval-guided Dialogue Response Generation via a Matching-to-Generation Framework (D19-1)

Challenge:	Recent studies have shown that a hybrid of self-attention networks (SANs) and recurrent neural networks (RNNs) outperforms both individual architectures, while not much is known about why the hybrid models work.
Approach:	They propose to use an advanced variant of self-attention networks (SANs) to enhance the strength of hybrid models by introducing a syntax-oriented inductive bias to perform tree-like composition.
Outcome:	The proposed model outperforms both individual models and a standard hybrid model on a machine translation task.

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Deng Cai, Yan Wang, Wei Bi, Zhaopeng Tu, Xiaojiang Liu, Shuming Shi

TexSmart: A System for Enhanced Natural Language Understanding (2021.acl-demo)

Challenge:	generative models for end-to-end sequence generation have been shown promising for this task . however, how to precisely extract a skeleton and how to effectively train a retrieval-guided response generator is still challenging.
Approach:	They propose a framework where skeleton extraction is made by an interpretable matching model and a retrieval-guided response generator is followed by a separate generator.
Outcome:	The proposed framework outperforms baseline models in a variety of experiments.

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Lemao Liu, Haisong Zhang, Haiyun Jiang, Yangming Li, Enbo Zhao, Kun Xu, Linfeng Song, Suncong Zheng, Botong Zhou, Dick Zhu, Xiao Feng, Tao Chen, Tao Yang, Dong Yu, Feng Zhang, ZhanHui Kang, Shuming Shi

GWLAN: General Word-Level AutocompletioN for Computer-Aided Translation (2021.acl-long)

Challenge:	TexSmart supports fine-grained named entity recognition (NER) Large-scale fine-granular entity types are expected to provide richer semantic information for downstream NLP applications.
Approach:	They introduce TexSmart, a text understanding system that supports fine-grained named entity recognition (NER) and enhanced semantic analysis functionalities.
Outcome:	The proposed system supports fine-grained named entity recognition (NER) and enhanced semantic analysis functions.

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Huayang Li, Lemao Liu, Guoping Huang, Shuming Shi

Reasons to Reject? Aligning Language Models with Judgments (2024.findings-acl)

Challenge:	Computer-aided translation (CAT) is a form of software that assists a human translator in the translation process.
Approach:	They propose to use computer-aided translation (CAT) to assist a human translator in the translation process.
Outcome:	The proposed method can give significantly more accurate predictions than baseline methods on CAT datasets.

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Weiwen Xu, Deng Cai, Zhisong Zhang, Wai Lam, Shuming Shi

Self-Attention with Structural Position Representations (D19-1)

Challenge:	a new framework for aligning large language models with judgments is proposed to help with alignment . a framework that allows for fine-grained inappropriate content detection and correction based on judgments . large language model alignment is critical for making artificial intelligence a reliable ally for humanity .
Approach:	They propose a framework that allows for fine-grained inappropriate content detection and correction based on judgments.
Outcome:	The proposed framework beats the 175B DaVinci003 and improves on AlpacaEval using judgments.

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Xing Wang, Zhaopeng Tu, Longyue Wang, Shuming Shi

Evaluating Explanation Methods for Neural Machine Translation (2020.acl-main)

Challenge:	Experimental results show that SANs can't encode positions of input words . SAN's are currently lacking in encoding positions of words based on position-unaware "bagof-words" theory .
Approach:	They propose to augment SANs with structural position representations to capture latent structure of input sentence.
Outcome:	The proposed approach consistently outperforms the sequential representations on translation tasks.

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Jierui Li, Lemao Liu, Huayang Li, Guanlin Li, Guoping Huang, Shuming Shi

Improving Machine Translation with Human Feedback: An Exploration of Quality Estimation as a Reward Model (2024.naacl-long)

Challenge:	Neural machine translation (NMT) has seen great success during recent years.
Approach:	They propose a metric that measures the fidelity of explanation methods on translation tasks . they use an efficient approximation to evaluate several explanation methods .
Outcome:	The proposed metric is efficient and can be used on translation tasks.

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Zhiwei He, Xing Wang, Wenxiang Jiao, Zhuosheng Zhang, Rui Wang, Shuming Shi, Zhaopeng Tu

Tail-to-Tail Non-Autoregressive Sequence Prediction for Chinese Grammatical Error Correction (2021.acl-long)

Challenge:	Existing methods to improve translation quality using human feedback have not been validated.
Approach:	They propose to use quality estimation to predict human preferences for feedback training . they propose to detect incorrect translations and assign a penalty term to the reward scores .
Outcome:	The proposed method outperforms systems using larger parallel corpora by a small amount of monolingual data.

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Piji Li, Shuming Shi

Syntactic Text Processing

Challenge:	Experimental results demonstrate the effectiveness of Tail-to-Tail (TtT) non-autoregressive sequence prediction for Chinese Grammatical Error Correction (CGEC)
Approach:	They propose a framework for Chinese Grammatical Error Correction (CGEC) that uses a BERT-initialized Transformer Encoder to model the error positions.
Outcome:	The proposed framework solves the problem of Chinese Grammatical Error Correction (CGEC) by modeling the token dependencies.

On the Branching Bias of Syntax Extracted from Pre-trained Language Models (2020.findings-emnlp)

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Huayang Li, Lemao Liu, Guoping Huang, Shuming Shi

Learning from Sibling Mentions with Scalable Graph Inference in Fine-Grained Entity Typing (2022.acl-long)

Challenge:	Existing methods for extracting constituency trees from language models suffer from branching bias.
Approach:	They propose to measure the branching bias by comparing the performance gap on a language and its reversed language.
Outcome:	The proposed method is agnostic to language models and extracting methods, and it can be implemented with three factors to introduce the branching bias.

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Yi Chen, Jiayang Cheng, Haiyun Jiang, Lemao Liu, Haisong Zhang, Shuming Shi, Ruifeng Xu

Structured Data in NLP

Challenge:	Existing models struggle to handle hard mentions due to insufficient contexts, limiting their overall typing performance.
Approach:	They propose to exploit sibling mentions to enhance the mention representations by adding unseen test mentions as new nodes for inference.
Outcome:	The proposed model outperforms ten strong baseline models and outperformed strong baselines.

One Model to Learn Both: Zero Pronoun Prediction and Translation (D19-1)

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Longyue Wang, Zhaopeng Tu, Xing Wang, Shuming Shi

Mixture of Heterogeneous Grouped Experts for Language Modeling (2026.acl-industry)

Challenge:	Zero pronouns (ZPs) are often omitted in pro-drop languages, but should be recalled in non-pro-drop language.
Approach:	They propose a unified and discourse-aware ZP translation approach for neural MT models . they jointly learn to predict and translate ZPs in an end-to-end manner .
Outcome:	The proposed method improves translation performance and ZP prediction accuracy over baseline models and external models.

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Zhicheng Ma, Xiang Liu, Zhaoxiang Liu, Ning Wang, Yi Shen, Kai Wang, Shuming Shi, Shiguo Lian

On the Sub-layer Functionalities of Transformer Decoder (2020.findings-emnlp)

Challenge:	Large Language Models (LLMs) based on Mixture-of-Experts (MoE) enforce uniform expert sizes, creating a rigidity that fails to align computational costs with varying token-level complexity.
Approach:	They propose a mixture of heterogeneous grouped experts (MoHGE) that allows for flexible, resource-aware expert combinations.
Outcome:	The proposed model matches the performance of existing Mixture-of-Experts architectures while maintaining balanced GPU utilization.

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Yilin Yang, Longyue Wang, Shuming Shi, Prasad Tadepalli, Stefan Lee, Zhaopeng Tu

Automatic Article Commenting: the Task and Dataset (P18-2)

Challenge:	Existing efforts to interpret the encoder of Transformer-based encoder-decoder architectures for neural machine translation have focused on assessing the encoded representations or interpreting the multi-head self-attentions.
Approach:	They propose to use Transformer-based encoder-decoder architectures to analyze how information is propagated through each module of each decoder layer.
Outcome:	The proposed model can be dropped with minimal loss of performance on three translation datasets and can be used to train and inference faster.

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Lianhui Qin, Lemao Liu, Wei Bi, Yan Wang, Xiaojiang Liu, Zhiting Hu, Hai Zhao, Shuming Shi

Challenge:	Existing methods to make comments on articles are based on human-annotated subsets, but they are not suitable for online forums.
Approach:	They propose to use a large-scale Chinese corpus with millions of real comments and a human-annotated subset characterizing the comments’ varying quality to generalize a broad set of popular reference-based metrics.
Outcome:	The proposed model incorporates human-annotated subset characterizing the comments’ varying quality and shows that it is more accurate than previous models.

BiTIIMT: A Bilingual Text-infilling Method for Interactive Machine Translation (2022.acl-long)

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Yanling Xiao, Lemao Liu, Guoping Huang, Qu Cui, Shujian Huang, Shuming Shi, Jiajun Chen

MCPG: A Flexible Multi-Level Controllable Framework for Unsupervised Paraphrase Generation (2022.findings-emnlp)

Challenge:	Existing IMT systems relying on lexical constrained decoding (LCD) are limited in translation efficiency and quality due to LCD.
Approach:	They propose a novel interactive neural machine translation system that uses lexical constraints to decode missing words in a manually revised translation.
Outcome:	The proposed system performs significantly better and faster than state-of-the-art IMT on three translation tasks.

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Yi Chen, Haiyun Jiang, Lemao Liu, Rui Wang, Shuming Shi, Ruifeng Xu

Exploring and Adapting Chinese GPT to Pinyin Input Method (2022.acl-long)

Challenge:	Existing studies on controllable unsupervised paraphrase generation are expensive and require supervised training on large parallel corpora.
Approach:	They propose a method for controllable unsupervised paraphrase generation that is flexible to adapt to specific domains without extra training.
Outcome:	The proposed method outperforms state-of-the-art unsupervised baselines by a margin.

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Minghuan Tan, Yong Dai, Duyu Tang, Zhangyin Feng, Guoping Huang, Jing Jiang, Jiwei Li, Shuming Shi

Towards Less Generic Responses in Neural Conversation Models: A Statistical Re-weighting Method (D18-1)

Challenge:	a frozen GPT can generate state-of-the-art performance on perfect pinyin, but performance drops when input includes abbreviated pinyan, which links to even larger number of Chinese characters.
Approach:	They propose to use Chinese GPT to generate fluent sentences using abbreviated pinyin.
Outcome:	The proposed approach improves on abbreviated pinyin across all domains.

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Yahui Liu, Wei Bi, Jun Gao, Xiaojiang Liu, Jian Yao, Shuming Shi

A Frustratingly Simple Decoding Method for Neural Text Generation (2024.lrec-main)

Challenge:	Experimental results show that Sequence-to-sequence models tend to generate generic/dull responses .
Approach:	They propose a statistical re-weighting method that assigns different weights for multiple responses of the same query.
Outcome:	The proposed method improves acceptance rate of generated responses and significantly reduces generated generic responses.

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Haoran Yang, Deng Cai, Huayang Li, Wei Bi, Wai Lam, Shuming Shi

Spotting AI’s Touch: Identifying LLM-Paraphrased Spans in Text (2024.findings-acl)

Challenge:	Neural text generation is notorious for repetitive loops and tedious outputs.
Approach:	They propose a method that penalizes future generation of repetitive content . they construct an anti-LM based on previously generated text .
Outcome:	The proposed method outperforms established baselines in terms of generation quality, decoding speed, and universality.

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Yafu Li, Zhilin Wang, Leyang Cui, Wei Bi, Shuming Shi, Yue Zhang

Towards Efficient Dialogue Pre-training with Transferable and Interpretable Latent Structure (2022.emnlp-main)

Challenge:	Existing work focuses on detecting (partially) AI-generated texts, but paraphrasing is commonly employed in various application scenarios for text refinement and diversity.
Approach:	They propose a framework for paraphrased text span detection that takes in the full text and assigns each sentence with a score indicating the paraphrasing degree.
Outcome:	The proposed framework can detect paraphrased text spans within a text . it takes in the full text and assigns each sentence with a score indicating the paraphrasing degree.

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Xueliang Zhao, Lemao Liu, Tingchen Fu, Shuming Shi, Dongyan Zhao, Rui Yan

An Empirical Study on Multiple Information Sources for Zero-Shot Fine-Grained Entity Typing (2021.emnlp-main)

Challenge:	Existing models that use millions of parameters on massive data are inefficient and lack interpretability.
Approach:	They propose a model with a latent structure that is easily transferable from the general domain to downstream tasks in a lightweight and transparent way.
Outcome:	The proposed model performs better than four strong baseline models in terms of automatic and human evaluations and is 5x faster than the strongest baseline model.

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Yi Chen, Haiyun Jiang, Lemao Liu, Shuming Shi, Chuang Fan, Min Yang, Ruifeng Xu

DoG-Instruct: Towards Premium Instruction-Tuning Data via Text-Grounded Instruction Wrapping (2024.naacl-long)

Challenge:	Auxiliary information from multiple sources has been demonstrated to be effective in zero-shot fine-grained entity typing (ZFET) however, there is no comprehensive understanding of how to make better use of the existing information sources and how they affect the performance of ZFET.
Approach:	They propose a multi-source fusion model targeting auxiliary information from multiple sources to improve zero-shot fine-grained entity typing (ZFET)
Outcome:	The proposed model achieves 11.42% and 22.84% gains over state-of-the-art baselines on BBN and Wiki respectively with regard to macro F1 scores.

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Yongrui Chen, Haiyun Jiang, Xinting Huang, Shuming Shi, Guilin Qi

Reasoning

Challenge:	Existing methods to collect high-quality instruction-response pairs suffer from unaffordable labor costs or severe hallucinations in the self-generation of LLMs.
Approach:	They propose a method that trains LLMs to generate instruction-response pairs based on human-written documents rather than relying solely on self-generation without context.
Outcome:	The proposed method outperforms existing typical methods on multiple benchmarks and shows that it is 100% scalable.

Advancement in Graph Understanding: A Multimodal Benchmark and Fine-Tuning of Vision-Language Models (2024.acl-long)

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Qihang Ai, Jiafan Li, Jincheng Dai, Jianwu Zhou, Lemao Liu, Haiyun Jiang, Shuming Shi

Salute the Classic: Revisiting Challenges of Machine Translation in the Age of Large Language Models (2025.tacl-1)

Challenge:	Graph data organizes complex relationships and interactions between objects . Graph neural networks (GNNs) are becoming more popular in graph learning .
Approach:	They propose a new paradigm for interactive and instructional graph data understanding and reasoning . they first evaluate the capabilities of public VLMs in graph learning from multiple aspects .
Outcome:	The proposed model achieves an accuracy increase of 5%-15% compared to baseline models . the best-performing model achieve scores comparable to Gemini in GPT-asissted Evaluation .

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Jianhui Pang, Fanghua Ye, Derek Fai Wong, Dian Yu, Shuming Shi, Zhaopeng Tu, Longyue Wang

Benchmarking and Improving Long-Text Translation with Large Language Models (2024.findings-acl)

Challenge:	a recent study revisits six core challenges that have influenced the evolution of Neural Machine Translation (NMT) domain mismatch, amount of parallel data, rare word prediction, translation of long sentences and sub-optimal beam search remain challenges in LLMs.
Approach:	They revisit core challenges that have acted as benchmarks for progress in NMT . they propose to revisit these challenges and offer insights into their relevance .
Outcome:	The proposed models significantly improve translation of sentences containing approximately 80 words, even translating documents up to 512 words.

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Longyue Wang, Zefeng Du, Wenxiang Jiao, Chenyang Lyu, Jianhui Pang, Leyang Cui, Kaiqiang Song, Derek Wong, Shuming Shi, Zhaopeng Tu

Document-Level Machine Translation with Large Language Models (2023.emnlp-main)

Challenge:	Recent studies have illuminated the promising capabilities of large language models (LLMs) in handling long texts.
Approach:	They construct a benchmark dataset specifically designed for the finetuning and evaluation of large language models (LLMs) they compare LLMs with MT models and find they exhibit shortcomings in long-text domains .
Outcome:	The proposed model performs better in long-text translation, and its performance diminishes as document size increases.

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Longyue Wang, Chenyang Lyu, Tianbo Ji, Zhirui Zhang, Dian Yu, Shuming Shi, Zhaopeng Tu

Generating Classical Chinese Poems via Conditional Variational Autoencoder and Adversarial Training (D18-1)

Challenge:	Large language models (LLMs) such as ChatGPT can produce coherent, cohesive, relevant, and fluent answers for various natural language processing tasks.
Approach:	They examine the impact of different prompts on document-level translation quality and discourse phenomena using figures and lines, which are invisible to GPT-4.
Outcome:	The proposed models outperform commercial MT systems and advanced document-level MT methods on a number of benchmarks and show potential to become a new paradigm for document- level translation.

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Juntao Li, Yan Song, Haisong Zhang, Dongmin Chen, Shuming Shi, Dongyan Zhao, Rui Yan

Robustness in NLP

Challenge:	Existing models for automatic poetry generation lack term novelty and thematic consistency.
Approach:	They propose a conditional variational autoencoder with adversarial training for classical Chinese poem generation.
Outcome:	The proposed model outperforms existing models on a large poetry corpus on 'classical Chinese' . it generates poems with novel terms and learns their thematic consistency with their titles.

Bridging the Data Gap between Training and Inference for Unsupervised Neural Machine Translation (2022.acl-long)

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Zhiwei He, Xing Wang, Rui Wang, Shuming Shi, Zhaopeng Tu

Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active Exploration (2023.emnlp-main)

Challenge:	Experimental results show that backtranslation improves UNMT performance by reducing the data gap between training and inference.
Approach:	They propose an online method to remedy the source discrepancy between training and inference . they use pseudo parallel data with translated source and translated target to mimic inference scenario .
Outcome:	The proposed method outperforms baselines on several widely-used language pairs by remedying the style and content gaps.

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Fanqi Wan, Xinting Huang, Tao Yang, Xiaojun Quan, Wei Bi, Shuming Shi

GuoFeng: A Benchmark for Zero Pronoun Recovery and Translation (2022.emnlp-main)

Challenge:	Existing data for instruction-tuning are inadequate for a wide range of tasks, limiting the scope for nuanced comprehension and interactions within these domains.
Approach:	They propose to use Large Language Models to explore a multitude of variations or possibilities to improve instruction-tuning data by active exploration.
Outcome:	The proposed approach improves domain-specific instruction coverage and shows significant improvements over baselines.

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Mingzhou Xu, Longyue Wang, Derek F. Wong, Hongye Liu, Linfeng Song, Lidia S. Chao, Shuming Shi, Zhaopeng Tu

On the Copying Behaviors of Pre-Training for Neural Machine Translation (2021.findings-acl)

Challenge:	ZPs are often omitted when they can be pragmatically or grammatically inferred from intraand inter-sentential contexts.
Approach:	They propose a benchmark testset for target evaluation on Chinese-English ZP translation.
Outcome:	The proposed testset covers five genres and identifies current challenges for evaluation.

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Xuebo Liu, Longyue Wang, Derek F. Wong, Liang Ding, Lidia S. Chao, Shuming Shi, Zhaopeng Tu

On the Word Alignment from Neural Machine Translation (P19-1)

Challenge:	Existing studies show that initializing NMT models with pre-trained language models (LM) can speed up the model training and boost the model performance.
Approach:	They propose a method to control copying behaviors in NMT models by initializing them with pre-trained language models (LM) they propose to use a metric called copy ratio to control the copying behavior in decoding.
Outcome:	The proposed method improves translation performance by controlling copying behaviors for pre-training based models.

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Xintong Li, Guanlin Li, Lemao Liu, Max Meng, Shuming Shi

RobustGEC: Robust Grammatical Error Correction Against Subtle Context Perturbation (2023.emnlp-main)

Challenge:	Prior researches suggest that neural machine translation (NMT) captures word alignment through its attention mechanism, however, attention may fail to capture word alignment for some NMT models.
Approach:	They propose two methods to induce word alignment which are general and agnostic to specific NMT models.
Outcome:	The proposed methods induce much better word alignment than attention.

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Yue Zhang, Leyang Cui, Enbo Zhao, Wei Bi, Shuming Shi

Robustness in NLP

Challenge:	Grammatical Error Correction (GEC) systems perform well in academic benchmarks, but in practical applications they may not correct errors when users perform irrelevant modifications.
Approach:	They propose a benchmark to evaluate the context robustness of Grammatical Error Correction systems.
Outcome:	The proposed method improves the accuracy of errors corrected by human annotations.

Redistributing Low-Frequency Words: Making the Most of Monolingual Data in Non-Autoregressive Translation (2022.acl-long)

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Liang Ding, Longyue Wang, Shuming Shi, Dacheng Tao, Zhaopeng Tu

TextBind: Multi-turn Interleaved Multimodal Instruction-following in the Wild (2024.findings-acl)

Challenge:	Knowledge distillation (KD) is the preliminary step for training non-autoregressive translation models, but it can lose important information for translating low-frequency words.
Approach:	They propose a knowledge distillation method which trains NAT student on external monolingual data with AT teacher trained on the original bilingual data.
Outcome:	Extensive experiments on eight WMT benchmarks show that monolingual KD outperforms the standard KD by improving low-frequency word translation without introducing any computational cost.

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Huayang Li, Siheng Li, Deng Cai, Longyue Wang, Lemao Liu, Taro Watanabe, Yujiu Yang, Shuming Shi

On the Complementarity between Pre-Training and Back-Translation for Neural Machine Translation (2021.findings-emnlp)

Challenge:	Large language models with instruction-following capabilities have revolutionized the field of artificial intelligence.
Approach:	They propose an annotation-free framework for empowering large language models with instruction-following capabilities.
Outcome:	The proposed framework generates multi-turn multimodal instruction-response conversations from a language model.

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Xuebo Liu, Longyue Wang, Derek F. Wong, Liang Ding, Lidia S. Chao, Shuming Shi, Zhaopeng Tu

Investigating Data Variance in Evaluations of Automatic Machine Translation Metrics (2022.findings-acl)

Challenge:	Experimental results show that PT and BT are nicely complementary to each other.
Approach:	They introduce two probing tasks for PT and BT respectively and investigate their complementarity.
Outcome:	The proposed methods establish state-of-the-art on the WMT16 English-Romanian and English-Russian benchmarks.

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Jiannan Xiang, Huayang Li, Yahui Liu, Lemao Liu, Guoping Huang, Defu Lian, Shuming Shi

Segmenting Natural Language Sentences via Lexical Unit Analysis (2021.findings-emnlp)

Challenge:	Current evaluation methods focus on one dataset, e.g., Newstest dataset in each year’s WMT Metrics Shared Task.
Approach:	They propose to use a single dataset to evaluate the performance of automatic translation metrics.
Outcome:	The results show that the rankings of metrics vary when the evaluation is conducted on different datasets.

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Yangming Li, Lemao Liu, Shuming Shi

Syntactic Text Processing

Challenge:	Recent work on sequence segmentation models suffer from invalid predictions and a lack of consistency.
Approach:	They propose a unified span-based model that embeds every span and computes a score for each segmentation candidate.
Outcome:	The proposed model achieves state-of-the-art on 6 of the 3 tasks tested.

Directional Skip-Gram: Explicitly Distinguishing Left and Right Context for Word Embeddings (N18-2)

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Yan Song, Shuming Shi, Jing Li, Haisong Zhang

Representation Learning

Challenge:	Existing word embedding models are limited by semantic resources, which are hard to obtain or annotate.
Approach:	They propose a directional skip-gram model that explicitly distinguishes between left and right contexts in word prediction.
Outcome:	The proposed model outperforms other models on different datasets in semantic and syntactic evaluations.

The World is Not Binary: Learning to Rank with Grayscale Data for Dialogue Response Selection (2020.emnlp-main)

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Zibo Lin, Deng Cai, Yan Wang, Xiaojiang Liu, Haitao Zheng, Shuming Shi

Information Retrieval

Challenge:	Existing approaches to learning-to-rank response selection are suboptimal due to ignorance of diversity of response quality.
Approach:	They propose to use off-the-shelf response retrieval models as automatic grayscale data generators to train response selection models.
Outcome:	The proposed approach can be automated without human effort on grayscale data.

Improved Visual Story Generation with Adaptive Context Modeling (2023.findings-acl)

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Zhangyin Feng, Yuchen Ren, Xinmiao Yu, Xiaocheng Feng, Duyu Tang, Shuming Shi, Bing Qin

Towards Understanding Neural Machine Translation with Word Importance (D19-1)

Challenge:	a recent work shows that diffusion models generate images of high resolution and semantic consistency to text prompts.
Approach:	They propose a method that uses adaptive context modeling to improve leading system . they evaluate their method on pororoSV and FlintstonesSV datasets .
Outcome:	The proposed method achieves state-of-the-art FID scores on pororo and Flintstones datasets.

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Shilin He, Zhaopeng Tu, Xing Wang, Longyue Wang, Michael Lyu, Shuming Shi

Skeleton-to-Response: Dialogue Generation Guided by Retrieval Memory (N19-1)

Challenge:	Neural machine translation (NMT) has advanced the state-of-the-art on various language pairs, but the interpretability of NMT remains unsatisfactory.
Approach:	They propose to attribute NMT output to every input word using a gradient-based method to measure word importance.
Outcome:	The proposed method is superior on identifying input words with higher influence on translation performance.

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Deng Cai, Yan Wang, Wei Bi, Zhaopeng Tu, Xiaojiang Liu, Wai Lam, Shuming Shi

Unsupervised Keyphrase Extraction by Learning Neural Keyphrase Set Function (2023.findings-acl)

Challenge:	Existing generative dialogue models generate responses from input queries . however, the results are limited and the models are unsatisfactory .
Approach:	They propose a framework which exploits retrieval results via a skeleton-to-response paradigm . they extract a query skelet and use it to generate a new skele and response .
Outcome:	The proposed approach significantly improves the informativeness of the generated responses.

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Mingyang Song, Haiyun Jiang, Lemao Liu, Shuming Shi, Liping Jing

Exploiting Sentential Context for Neural Machine Translation (P19-1)

Challenge:	Unsupervised keyphrase extraction is a task of extracting a keyphrase set that provides readers with highlevel information about the key ideas or important topics described in the document.
Approach:	They propose an unsupervised keyphrase extraction task that is a document-set matching problem instead of modeling the relevance between an individual phrase and the document.
Outcome:	The proposed model outperforms the state-of-the-art unsupervised keyphrase extraction baselines by a large margin.

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Xing Wang, Zhaopeng Tu, Longyue Wang, Shuming Shi

Making Better Use of Training Corpus: Retrieval-based Aspect Sentiment Triplet Extraction via Label Interpolation (2023.findings-acl)

Challenge:	Existing approaches to exploit sentential context for machine translation are not well studied.
Approach:	They propose a shallow sentential context that exploits top encoder layer, and a deep sentential one that aggregates sentential representations from all internal layers.
Outcome:	The proposed model outperforms the strong Transformer model on the English-German and English-French benchmarks.

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Guoxin Yu, Lemao Liu, Haiyun Jiang, Shuming Shi, Xiang Ao

Understanding and Improving Hidden Representations for Neural Machine Translation (N19-1)

Challenge:	Existing methods to extract sentimental triplets are infeasible and counterproductive . aspect Sentiment Triplets Extraction (ASTE) task is an emerging sub-task of Aspect-based Sentimence Analysis .
Approach:	They propose a retrieval-based approach to the Aspect Sentiment Triplet Extraction task . they retrieve semantic similar triplets from the training corpus and interpolate their label information .
Outcome:	The proposed approach establishes a new state-of-the-art on the Aspect Sentiment Triplet Extraction task.

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Guanlin Li, Lemao Liu, Xintong Li, Conghui Zhu, Tiejun Zhao, Shuming Shi

A Model-agnostic Data Manipulation Method for Persona-based Dialogue Generation (2022.acl-long)

Challenge:	Existing studies have explored some methods for understanding hidden representations, but they have not sought to improve the translation quality rationally according to their understanding.
Approach:	They propose to construct a sequence of nested relative tasks and measure the feature generalization ability of the learned hidden representation over these tasks.
Outcome:	The proposed methods achieve consistent improvements (up to +1.3 BLEU) on two widely-used datasets.

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Yu Cao, Wei Bi, Meng Fang, Shuming Shi, Dacheng Tao

Retrieval-Augmented Few-shot Text Classification (2023.findings-emnlp)

Challenge:	Existing models for introducing explicit personas are expensive due to their expensive collection costs.
Approach:	They propose a data manipulation method which is model-agnostic to be packed with any persona-based dialogue generation model to improve their performance.
Outcome:	The proposed method is model-agnostic to be packed with any persona-based dialogue generation model to improve their performance.

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Guoxin Yu, Lemao Liu, Haiyun Jiang, Shuming Shi, Xiang Ao

Information Retrieval

Challenge:	Existing methods for retrieval-augmented text classification are successful in the few-shot scenario with limited retrieval space.
Approach:	They propose to use EM-L and R-L to provide task-specific guidance to retrieval metric . they also propose to incorporate retrieved memory alongside parameters for better generalization .
Outcome:	The proposed methods perform better on the few-shot scenario with limited retrieval space.

Effidit: An Assistant for Improving Writing Efficiency (2023.acl-demo)

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Shuming Shi, Enbo Zhao, Wei Bi, Deng Cai, Leyang Cui, Xinting Huang, Haiyun Jiang, Duyu Tang, Kaiqiang Song, Longyue Wang, Chenyan Huang, Guoping Huang, Yan Wang, Piji Li

Green & Sustainable NLP

Challenge:	Effidit is a digital writing assistant that provides three modules to help users write faster and more efficiently.
Approach:	They present Effidit, a digital writing assistant that provides three modules to help users write higher-quality text more efficiently.
Outcome:	Effidit expands the capabilities of a typical writing assistant by providing three modules . Effit can help users create their own text faster and more efficiently .

On the Inference Calibration of Neural Machine Translation (2020.acl-main)

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Shuo Wang, Zhaopeng Tu, Shuming Shi, Yang Liu

Alleviating Hallucinations of Large Language Models through Induced Hallucinations (2025.findings-naacl)

Challenge:	Existing studies show that NMT models trained with label smoothing are well-calibrated on ground-truth training data, but miscalibration remains a challenge during inference due to the discrepancy between training and inference.
Approach:	They propose a graduated label smoothing method that can improve inference calibration and translation performance.
Outcome:	The proposed method improves both inference calibration and translation performance.

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Yue Zhang, Leyang Cui, V. W., Shuming Shi

Reasoning

Challenge:	Existing studies have shown that large language models generate inaccurate or fabricated information, a phenomenon known as hallucinations.
Approach:	They propose a simple strategy to induce-then-contrast decode LLMs to enhance their factuality . they first induce hallucinations from the original model and penalize them .
Outcome:	The proposed strategy improves factuality of large language models across task formats, model sizes, and model families.

Explicit Syntactic Guidance for Neural Text Generation (2023.acl-long)

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Yafu Li, Leyang Cui, Jianhao Yan, Yongjing Yin, Wei Bi, Shuming Shi, Yue Zhang

Syntactic Text Processing

Challenge:	Existing text generation models follow the sequence-to-sequence paradigm . generative grammar suggests humans generate language by learning language grammar .
Approach:	They propose a syntax-guided generation schema that searches the syntax tree in a top-down direction.
Outcome:	The proposed method outperforms autoregressive baselines on paraphrase generation and machine translation.

Enhancing the Open-Domain Dialogue Evaluation in Latent Space (2021.findings-acl)

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Zhangming Chan, Lemao Liu, Juntao Li, Haisong Zhang, Dongyan Zhao, Shuming Shi, Rui Yan

MAGE: Machine-generated Text Detection in the Wild (2024.acl-long)

Challenge:	Existing methods to evaluate opendomain dialogues are limited due to the one-to-many nature of dialogues.
Approach:	They propose a self-supervised setting to obtain a smooth latent space that captures discourse-level context information and implicitly models more references in latent spaces.
Outcome:	The proposed method outperforms baseline methods on two real-world dialogue datasets.

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Yafu Li, Qintong Li, Leyang Cui, Wei Bi, Zhilin Wang, Longyue Wang, Linyi Yang, Shuming Shi, Yue Zhang

DAST: Difficulty-Adaptive Slow-Thinking for Large Reasoning Models (2025.emnlp-industry)

Challenge:	Existing research has focused on evaluating detection methods for specific domains or language models.
Approach:	They build a testbed to detect texts from diverse human writings and LLMs using different detection methods.
Outcome:	Empirical results show that the top performing detector can identify 84.12% out-of-domain texts generated by a new LLM, indicating the feasibility for application scenarios.

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Yi Shen, Jian Zhang, Jieyun Huang, Shuming Shi, Wenjing Zhang, Jiangze Yan, Ning Wang, Kai Wang, Zhaoxiang Liu, Shiguo Lian

Green & Sustainable NLP

Challenge:	Recent advances in slow-thinking reasoning models have shown exceptional performance in complex reasoning tasks.
Approach:	They propose a framework that enables models to automatically adjust Chain-of-Thought (CoT) length based on problem difficulty.
Outcome:	The proposed framework penalizes inefficiency on simple problems while incentivizing deep reasoning for complex ones.

Microblog Hashtag Generation via Encoding Conversation Contexts (N19-1)

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Yue Wang, Jing Li, Irwin King, Michael R. Lyu, Shuming Shi

Rigid Formats Controlled Text Generation (2020.acl-main)

Challenge:	Automated hashtag annotation plays an important role in content understanding for microblog posts.
Approach:	They propose to annotate hashtags with a novel sequence generation framework via viewing the hashtag as a short sequence of words.
Outcome:	The proposed model outperforms existing models on two large-scale datasets . it can generate rare and even unseen hashtags, which is not possible with existing models .

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Piji Li, Haisong Zhang, Xiaojiang Liu, Shuming Shi

Semi-supervised Text Style Transfer: Cross Projection in Latent Space (D19-1)

Challenge:	Neural text generation is a challenging task that requires rigid formats to be controlled . a framework called SongNet is designed to tackle this problem .
Approach:	They propose a framework to tackle a task called rigid formats controlled text generation . they propose rhyming schemes and a transformer-based auto-regressive language model to improve the modeling performance .
Outcome:	The proposed framework improves the performance on format, rhyme, and sentence integrity.

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Mingyue Shang, Piji Li, Zhenxin Fu, Lidong Bing, Dongyan Zhao, Shuming Shi, Rui Yan

On the Language Coverage Bias for Neural Machine Translation (2021.findings-acl)

Challenge:	Text style transfer task has long suffered from the shortage of parallel data .
Approach:	They propose a semi-supervised text style transfer model that combines parallel data with large-scale nonparallel data to train it.
Outcome:	The proposed model can transfer a sentence of one style to another while retaining its original content meaning while preserving its original meaning.

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Shuo Wang, Zhaopeng Tu, Zhixing Tan, Shuming Shi, Maosong Sun, Yang Liu

Rethinking Word-Level Auto-Completion in Computer-Aided Translation (2023.emnlp-main)

Challenge:	Language coverage bias is important for neural machine translation because of the target-original training data.
Approach:	They propose two approaches to alleviate the language coverage bias problem by explicitly distinguishing between the source-and target-original training data.
Outcome:	The proposed methods improve translation tasks on both back-and forward-translation and their tagged variants.

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Xingyu Chen, Lemao Liu, Guoping Huang, Zhirui Zhang, Mingming Yang, Shuming Shi, Rui Wang

On the Evaluation Metrics for Paraphrase Generation (2022.emnlp-main)

Challenge:	Existing models for word-level auto-completion (WLAC) do not meet the criterion of good auto-completes.
Approach:	They propose a measurable criterion to address the question: what kind of words are good auto-completions? they propose an approach to enhance WLAC performance by promoting adherence to the cri-terion.
Outcome:	The proposed approach outperforms the top-performing system submitted to the WLAC shared tasks in WMT2022 while using significantly smaller model sizes.

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Lingfeng Shen, Lemao Liu, Haiyun Jiang, Shuming Shi

Exploiting Deep Representations for Neural Machine Translation (D18-1)

Challenge:	Existing evaluation metrics for paraphrase generation are not designed for the task, but adopted from other evaluation tasks.
Approach:	They propose a new evaluation metric for paraphrase generation that uses reference-based and reference-free metrics.
Outcome:	The proposed evaluation metric outperforms existing metrics and is more reliable than reference-based metrics.

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Zi-Yi Dou, Zhaopeng Tu, Xing Wang, Shuming Shi, Tong Zhang

Self-Training Sampling with Monolingual Data Uncertainty for Neural Machine Translation (2021.acl-long)

Challenge:	Neural machine translation models typically implement encoder and decoder as multiple layers, but only the top layers are leveraged in the subsequent process, which misses the opportunity to exploit useful information embedded in other layers.
Approach:	They propose to expose all of these signals with layer aggregation and multi-layer attention mechanisms and introduce an auxiliary regularization term to encourage different layers to capture diverse information.
Outcome:	The proposed approach exposes all of these signals with layer aggregation and multi-layer attention mechanisms on widely-used translation datasets.

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Wenxiang Jiao, Xing Wang, Zhaopeng Tu, Shuming Shi, Michael Lyu, Irwin King

Selection-p: Self-Supervised Task-Agnostic Prompt Compression for Faithfulness and Transferability (2024.findings-emnlp)

Challenge:	Experimental results show that enhancing the learning on uncertain monolingual sentences improves the translation quality of high-uncertainty sentences and also benefits the prediction of low-frequency words at the target side.
Approach:	They propose to use monolingual data to augment model training with synthetic parallel data by selecting the most informative monolingual sentences to complement the parallel data.
Outcome:	The proposed approach improves the performance of natural language models by selecting the most informative monolingual sentences.

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Tsz Ting Chung, Leyang Cui, Lemao Liu, Xinting Huang, Shuming Shi, Dit-Yan Yeung

Low-Resource NLP

Challenge:	Large Language Models (LLMs) have demonstrated impressive capabilities when leveraging in-context learning.
Approach:	They propose a method that discretizes uninformative tokens using a self-supervised pre-training technique.
Outcome:	The proposed method achieves state-of-the-art performance across classification tasks while requiring only 0.8% decrease in performance.

A Discrete CVAE for Response Generation on Short-Text Conversation (D19-1)

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Jun Gao, Wei Bi, Xiaojiang Liu, Junhui Li, Guodong Zhou, Shuming Shi

SORTIE: Dependency-Aware Symbolic Reasoning for Logical Data-to-text Generation (2023.findings-acl)

Challenge:	Neural conversation models are easy to generate bland and generic responses . however, their improvement of generating high-quality responses is still unsatisfactory .
Approach:	They propose to use a discrete latent variable with an explicit semantic meaning to improve the conditional variational autoencoder on short-text conversation.
Outcome:	The proposed model outperforms various kinds of generation models under automatic and human evaluations and generates more diverse and informative responses.

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Xueliang Zhao, Tingchen Fu, Lemao Liu, Lingpeng Kong, Shuming Shi, Rui Yan

Understanding and Improving Sequence-to-Sequence Pretraining for Neural Machine Translation (2022.acl-long)

Challenge:	Existing studies on logical data-to-text generation rely on neural language models to generate the final table description, but they have difficulty working out key entities in the description.
Approach:	They propose a symbolic reasoning framework that reasons out each entity in the table description with a table-compatible programming language.
Outcome:	The proposed framework outperforms existing methods on three datasets and three backbones with an absolute improvement of 5.7%11.5% on SP-Acc.

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Wenxuan Wang, Wenxiang Jiao, Yongchang Hao, Xing Wang, Shuming Shi, Zhaopeng Tu, Michael Lyu

Fine-Grained Sentence Functions for Short-Text Conversation (P19-1)

Challenge:	Existing studies on self-supervised pretraining for machine translation have focused on the jointly pretrained decoder .
Approach:	They propose a method to improve neural machine translation by jointly pretrained decoder . they propose two strategies to remedy the domain and objective discrepancies .
Outcome:	The proposed approach improves translation performance and model robustness on three language pairs.

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Wei Bi, Jun Gao, Xiaojiang Liu, Shuming Shi

When Hearst Is not Enough: Improving Hypernymy Detection from Corpus with Distributional Models (2020.emnlp-main)

Challenge:	Existing research has analyzed various factors indicating the conversational purpose such as emotions, topics, word orders, syntactic patterns and other aspects.
Approach:	They propose to annotate a short-text conversation dataset with annotated sentences and train conversation models conditioned on the sentence functions.
Outcome:	The proposed model can predict the quality of the returned responses.

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Changlong Yu, Jialong Han, Peifeng Wang, Yangqiu Song, Hongming Zhang, Wilfred Ng, Shuming Shi

Disperse-Then-Merge: Pushing the Limits of Instruction Tuning via Alignment Tax Reduction (2024.findings-acl)

Challenge:	a taxonomy is a semantic hierarchy of words or concepts organized w.r.t. their hypernymy relationships.
Approach:	They propose a framework for hypernymy detection using large textual corpora . they quantify the non-negligible existence of specific sparsity cases .
Outcome:	The proposed framework quantifies the non-negligible existence of specific sparsity cases on several benchmark datasets.

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Tingchen Fu, Deng Cai, Lemao Liu, Shuming Shi, Rui Yan

Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate (2024.emnlp-main)

Challenge:	Pre-trained language models may not follow human instructions and produce toxic, hallucinated, or biased content.
Approach:	They propose a disperse-then-merge framework that dispersers instruction-following data into portions and trains multiple sub-models using different data portions.
Outcome:	The proposed framework outperforms data curation and training regularization on standard knowledge and reasoning benchmarks.

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Tian Liang, Zhiwei He, Wenxiang Jiao, Xing Wang, Yan Wang, Rui Wang, Yujiu Yang, Shuming Shi, Zhaopeng Tu

Dialogue Response Selection with Hierarchical Curriculum Learning (2021.acl-long)

Challenge:	Modern large language models (LLMs) have shown remarkable performance on general language tasks but struggle on complex reasoning tasks.
Approach:	They propose a multi-agent debate framework that encourages divergent thinking in LLMs . they propose to break debate and use a judge to obtain a final solution .
Outcome:	The proposed framework encourages divergent thinking in large language models . it is able to generate novel thoughts even if initial stance is incorrect .

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Yixuan Su, Deng Cai, Qingyu Zhou, Zibo Lin, Simon Baker, Yunbo Cao, Shuming Shi, Nigel Collier, Yan Wang

Not All Preference Pairs Are Created Equal: A Recipe for Annotation-Efficient Iterative Preference Learning (2024.findings-emnlp)

Challenge:	Empirical studies on three benchmark datasets with three state-of-the-art matching models demonstrate that the proposed learning framework significantly improves the model performance across various evaluation metrics.
Approach:	They propose a hierarchical curriculum learning framework that trains matching models in an “easy-to-difficult” scheme.
Outcome:	The proposed framework significantly improves the model performance across evaluation metrics on three benchmark datasets with three state-of-the-art matching models.

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Sen Yang, Leyang Cui, Deng Cai, Xinting Huang, Shuming Shi, Wai Lam

Multi-Granularity Self-Attention for Neural Machine Translation (D19-1)

Challenge:	Large language models (LLMs) have shown remarkable capabilities to understand and generate human languages, supporting applications such as question answering, coding, and psychological counseling.
Approach:	They propose strategies to save annotation budgets while achieving competitive or even better performances for iterative preference learning.
Outcome:	The proposed methods save annotation budgets while achieving better performance.

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Jie Hao, Xing Wang, Shuming Shi, Jinfeng Zhang, Zhaopeng Tu

QuaSE: Sequence Editing under Quantifiable Guidance (D18-1)

Challenge:	Existing neural machine translation models use a deep multi-head self-attention network with no explicit phrase information.
Approach:	They propose a neural network that combines multi-head self-attention and phrase modeling to train attention heads to attend to phrases in either n-gram or syntactic formalisms.
Outcome:	The proposed approach improves on English-to-German and NIST Chinese-to English translation tasks.

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Yi Liao, Lidong Bing, Piji Li, Shuming Shi, Wai Lam, Tong Zhang

Challenge:	Existing methods for Quantifiable Sequence Editing (QuaSE) require editing an input sequence to generate an output that satisfies a numerical outcome value measuring a certain property of the sequence.
Approach:	They propose a framework for Quantifiable Sequence Editing that allows editing an input sequence to change an outcome and keep the content.
Outcome:	The proposed framework disentangles outcome factor and content factor from the input sentence to allow editing to change the outcome and keep the content.

Rethinking Translation Memory Augmented Neural Machine Translation (2023.findings-acl)

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Hongkun Hao, Guoping Huang, Lemao Liu, Zhirui Zhang, Shuming Shi, Rui Wang

Addressing Entity Translation Problem via Translation Difficulty and Context Diversity (2024.findings-acl)

Challenge:	Existing approaches to enhance neural machine translation (NMT) by using a TM have been reported to be effective.
Approach:	They propose a translation memory augmented neural machine translation model that is good at fitting data but more sensitive to fluctuations in training data.
Outcome:	The proposed model achieves consistent gains over conventional and existing models under two variance-preferable scenarios as well as the high resource scenario.

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Tian Liang, Xing Wang, Mingming Yang, Yujiu Yang, Shuming Shi, Zhaopeng Tu

On Synthetic Data for Back Translation (2022.naacl-main)

Challenge:	Neural machine translation systems often produce inadequate translations for named entities.
Approach:	They propose a data augmentation strategy to enhance the accuracy of named entity translation by retraining the target named entity pair.
Outcome:	The proposed method improves translation accuracy across test sets and terminology tests.

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Jiahao Xu, Yubin Ruan, Wei Bi, Guoping Huang, Shuming Shi, Lihui Chen, Lemao Liu

Fine-grained Entity Typing without Knowledge Base (2021.emnlp-main)

Challenge:	Existing studies on back translation (BT) focus on beam search or random sampling . a new method to generate synthetic data with a backward model is proposed to improve BT performance.
Approach:	They propose a method to generate synthetic data to trade off quality and importance factors . back translation (BT) is one of the most significant technologies in NMT research fields .
Outcome:	The proposed method outperforms the baseline methods on WMT14 DE-EN, EN-DE, and RU-EN benchmark tasks.

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Jing Qian, Yibin Liu, Lemao Liu, Yangming Li, Haiyun Jiang, Haisong Zhang, Shuming Shi

Knowledge Representation

Challenge:	Existing work on fine-grained entity typing (FET) relies on knowledge bases as distant supervision, but lack of or incompleteness of KB can hinder training.
Approach:	They propose a two-step framework that trains FET models without accessing any knowledge base.
Outcome:	The proposed framework achieves competitive performance with respect to the models trained on the original KB-supervised datasets.

Target Foresight Based Attention for Neural Machine Translation (N18-1)

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Xintong Li, Lemao Liu, Zhaopeng Tu, Shuming Shi, Max Meng

Fuzzy Reasoning Chain (FRC): An Innovative Reasoning Framework from Fuzziness to Clarity (2025.findings-emnlp)

Challenge:	Empirical experiments on Chinese-to-English and Japanese-to English datasets show that the proposed attention model delivers significant improvements in terms of alignment error rate and BLEU.
Approach:	They propose to explicitly access the target foresight word in the attention model to improve alignment and translation accuracy.
Outcome:	Empirical results show that the proposed model improves alignment error rate and BLEU on Chinese-to-English and Japanese-toEnglish datasets.

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Ping Chen, Xiang Liu, Zhaoxiang Liu, Zezhou Chen, Xingpeng Zhang, Huan Hu, Zipeng Wang, Kai Wang, Shuming Shi, Shiguo Lian