Papers by Tong Zhu

UAQFact: Evaluating Factual Knowledge Utilization of LLMs on Unanswerable Questions (2025.findings-acl)

Challenge:	Recent training-based TTS methods, such as continued reinforcement learning, have surged in popularity, while training-free TTS approaches are gradually fading from prominence.
Approach:	They propose a fine-grained sequential scaling method guided by process verification that integrates training-free TTS methods with other classical parallel scaling methods at the step level.
Outcome:	Experiments on five instruction-tuned large language models (LLMs) show that training-free TTS methods can extend reasoning performance boundaries.

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Chuanyuan Tan, Wenbiao Shao, Hao Xiong, Tong Zhu, Zhenhua Liu, Kai Shi, Wenliang Chen

Knowledge Representation

Challenge:	Existing datasets to assess LLMs' performance on unanswerable questions lack factual knowledge support.
Approach:	They propose a bilingual unanswerable question dataset with auxiliary factual knowledge created from a Knowledge Graph and two new tasks to measure LLMs' ability to utilize internal and external factual information.
Outcome:	The proposed datasets show that LLMs do not consistently perform well even when they have factual knowledge stored.

TWAG: A Topic-Guided Wikipedia Abstract Generator (2021.acl-long)

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Fangwei Zhu, Shangqing Tu, Jiaxin Shi, Juanzi Li, Lei Hou, Tong Cui

Leveraging Unit Language Guidance to Advance Speech Modeling in Textless Speech-to-Speech Translation (2025.findings-acl)

Challenge:	Existing models view Wikipedia abstract as plain text, ignoring that it is a description of a certain entity and can be decomposed into different topics.
Approach:	They propose a model that guides Wikipedia abstract generation with topical information.
Outcome:	The proposed model outperforms baselines and is capable of generating comprehensive abstracts.

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Yuhao Zhang, Xiangnan Ma, Kaiqi Kou, Peizhuo Liu, Weiqiao Shan, Benyou Wang, Tong Xiao, Yuxin Huang, Zhengtao Yu, JingBo Zhu

Shared-Private Bilingual Word Embeddings for Neural Machine Translation (P19-1)

Challenge:	Existing textless speech-to-speech translation models have two main challenges: 1) learning cross-modal features and 2) learning alignment of difference languages in long sequences.
Approach:	They propose a unit language to overcome two main modeling challenges . they propose task prompt modeling to utilize the unit language in guiding the modeling process.
Outcome:	The proposed language improves over a strong baseline and achieves comparable performance to models trained with text.

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Xuebo Liu, Derek F. Wong, Yang Liu, Lidia S. Chao, Tong Xiao, Jingbo Zhu

Weight Distillation: Transferring the Knowledge in Neural Network Parameters (2021.acl-long)

Challenge:	Word embedding is central to neural machine translation, but indirectly interfaces with other layers, making them comparatively isolated.
Approach:	They propose a shared-private bilingual word embedding which gives a closer relationship between the source and target embedders and reduces the number of model parameters.
Outcome:	The proposed model improves on 5 language pairs belonging to 6 different language families and written in 5 different alphabets and significantly reduces model parameters.

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Ye Lin, Yanyang Li, Ziyang Wang, Bei Li, Quan Du, Tong Xiao, Jingbo Zhu

Modality Adaption or Regularization? A Case Study on End-to-End Speech Translation (2023.acl-short)

Challenge:	Knowledge distillation is an effective method for model acceleration and compression.
Approach:	They propose to use parameters to distill knowledge from large neural networks to small ones . they propose to do this by using a parameter generator to transfer the knowledge to a small neural network .
Outcome:	The proposed method learns a small network 1.88 2.94x faster than the large network but with competitive BLEU points.

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Yuchen Han, Chen Xu, Tong Xiao, Jingbo Zhu

Bag of Tricks for Optimizing Transformer Efficiency (2021.findings-emnlp)

Challenge:	End-to-end speech translation models have limited training data and are often inefficient due to the inconsistency of length and representation between speech and text.
Approach:	They find that the "modality gap" between speech and text data is not a major problem in E2E ST . they decouple the encoder to speech encoder and text encoder, and they find that there is a 'capacity gap'
Outcome:	The proposed model achieves 29.0 for en-de and 40.3 for fr on the MuST-C dataset.

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Ye Lin, Yanyang Li, Tong Xiao, Jingbo Zhu

The LLM Already Knows: Estimating LLM-Perceived Question Difficulty via Hidden Representations (2025.emnlp-main)

Challenge:	Improving Transformer efficiency has become increasingly attractive in recent years.
Approach:	They propose to combine pruning, quantization, new architectures and training strategies to improve Transformer efficiency.
Outcome:	The proposed methods improve the inference efficiency of a strong Transformer system by 3.80x on CPU and 2.52x on GPU.

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Yubo Zhu, Dongrui Liu, Zecheng Lin, Wei Tong, Sheng Zhong, Jing Shao

Green & Sustainable NLP

Challenge:	Existing methods for difficulty estimation rely on repeated response sampling, auxiliary models, or fine-tuning the target model itself.
Approach:	They propose a method that leverages only the hidden representations produced by large language models.
Outcome:	The proposed method outperforms baselines in difficulty estimation on textual and multimodal tasks and improves adaptive reasoning strategies with fewer generated tokens.

ExpertIVS: Sociological Expert Driven Individual Value Simulation in Large Language Models (2026.findings-acl)

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Zhen Wang, Yuqi Ren, Yuehan Cui, Hongxiang Wang, Jianxiang Peng, Zhaoxia Zhang, Bingkun Zhu, Tongxuan Zhang, Dezhi Tong, Deyi Xiong

A Simple and Effective Approach to Robust Unsupervised Bilingual Dictionary Induction (2020.coling-main)

Challenge:	Existing methods for social simulations mechanically stitch survey responses into prompts, which suffer from semantic fragmentation, failing to capture the internal coherence of human value systems.
Approach:	They propose a framework employing 14 Sociological Expert Agents to interpret World Values Survey responses through structured professional perspectives rather than direct responses concatenation.
Outcome:	Experiments on 480 individuals from 12 countries show that ExpertIVS outperforms baselines in value generalization and significantly outperfies the existing methods.

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Yanyang Li, Yingfeng Luo, Ye Lin, Quan Du, Huizhen Wang, Shujian Huang, Tong Xiao, Jingbo Zhu

Cross-layer Attention Sharing for Pre-trained Large Language Models (2026.tacl-1)

Challenge:	Recent work has questioned the robustness of unsupervised bilingual dictionary induction methods on distant language pairs.
Approach:	They propose an iterative dimension reduction method to bridge this gap . they propose a method that initializes and self-learning and inducing a dictionary .
Outcome:	The proposed method achieves 13.64 55.53% accuracy between English and four distant languages.

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Yongyu Mu, Yuzhang Wu, Yuchun Fan, Chenglong Wang, Hengyu Li, Jiali Zeng, Qiaozhi He, Murun Yang, Fandong Meng, Jie Zhou, Tong Xiao, Jingbo Zhu

FastMem: Fast Memorization of Prompt Improves Context Awareness of Large Language Models (2024.findings-emnlp)

Challenge:	Existing studies focus on compressing the Key-Value cache or grouping attention heads, while overlooking redundancy between layers.
Approach:	They propose a lightweight substitute for self-attention in well-trained LLMs that uses feed-forward networks to align attention heads between adjacent layers and low-rank matrices to approximate differences in layer-wise attention weights.
Outcome:	The proposed model reduces redundancy by sharing weights across layers while maintaining high response quality while reducing redundant calculations within 53% 84% of the total layers.

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Junyi Zhu, Shuochen Liu, Yu Yu, Bo Tang, Yibo Yan, Zhiyu Li, Feiyu Xiong, Tong Xu, Matthew Blaschko

Granular Entity Mapper: Advancing Fine-grained Multimodal Named Entity Recognition and Grounding (2024.findings-emnlp)

Challenge:	Large language models struggle with context awareness, leading to inaccuracies in tasks requiring faithful adherence to provided information.
Approach:	They propose a method to enhance LLMs' context awareness by updating only the last Feed-Forward Network module to maximize the likelihood of the prompt before inference .
Outcome:	The proposed method improves the accuracy of Llama 3-8B-Inst on the NQ-SWAP dataset from 59.1% to 71.6% and reduces the output structure failure rate of Qwen 1.5-4B-Chat from 34.9% to 25.5%.

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Ziqi Wang, Chen Zhu, Zhi Zheng, Xinhang Li, Tong Xu, Yongyi He, Qi Liu, Ying Yu, Enhong Chen

R^3AG: Retriever Routing for Retrieval-Augmented Generation (2026.acl-long)

Challenge:	Existing methods for fine-grained content extraction are limited by long-tailed distribution of textual entity categories and performance of object detectors.
Approach:	They propose a multi-granularity entity recognition module and a reranking module to integrate hierarchical information of entity categories, visual cues, and external textual resources collectively.
Outcome:	The proposed framework achieves state-of-the-art on the fine-grained content extraction task.

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Tong Zhao, Yutao Zhu, Yucheng Tian, Zhicheng Dou

Information Retrieval

Challenge:	Retrieval-augmented generation (RAG) is often bottlenecked by the “one-size-fits-all” retrieval paradigm, as different queries exhibit distinct preferences for different retrievers.
Approach:	They propose a novel routing framework that explicitly models the dynamic alignment between queries and retriever capabilities and decomposes retriever capability into two learnable dimensions: retrieval quality and generation utility.
Outcome:	Experiments on knowledge-intensive tasks show that R3AG outperforms both the best individual retrievers and state-of-the-art static routing methods.

Multi-Path Transformer is Better: A Case Study on Neural Machine Translation (2022.findings-emnlp)

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Ye Lin, Shuhan Zhou, Yanyang Li, Anxiang Ma, Tong Xiao, Jingbo Zhu

RRNorm: A Novel Framework for Chinese Disease Diagnoses Normalization via LLM-Driven Terminology Component Recognition and Reconstruction (2024.findings-acl)

Challenge:	Extensive experiments on 12 WMT tasks show that shallower multi-path models can achieve similar or even better performance than the deeper model.
Approach:	They propose to use a parameter-efficient multi-path structure to fuse features extracted from different paths to achieve better performance.
Outcome:	The proposed model can achieve better performance with the same number of parameters than the deeper model.

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Yongqi Fan, Yansha Zhu, Kui Xue, Jingping Liu, Tong Ruan

Diversifying Content Generation for Commonsense Reasoning with Mixture of Knowledge Graph Experts (2022.findings-acl)

Challenge:	Clinical Terminology Normalization (CTN) aims at finding standard terms from a given termbase for mentions extracted from clinical texts.
Approach:	They propose a method that leverages reasoning capability of large language models to recognize components of terms and automate decomposition.
Outcome:	The proposed strategy achieves state-of-the-art on the experimental dataset.

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Wenhao Yu, Chenguang Zhu, Lianhui Qin, Zhihan Zhang, Tong Zhao, Meng Jiang

Enhancing Speech Large Language Models with Prompt-Aware Mixture of Audio Encoders (2025.emnlp-main)

Challenge:	Recent years have seen a surge of interest in improving the generation quality of commonsense reasoning tasks.
Approach:	They propose a method that diversifies the generative reasoning by a mixture of expert strategy on commonsense knowledge graphs to encourage various generation outputs.
Outcome:	The proposed method improves diversity while achieving on par performance on two GCR benchmarks, based on both automatic and human evaluations.

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Weiqiao Shan, Yuang Li, Yuhao Zhang, Yingfeng Luo, Chen Xu, Xiaofeng Zhao, Long Meng, Yunfei Lu, Min Zhang, Hao Yang, Tong Xiao, JingBo Zhu

EIT: Enhanced Interactive Transformer (2024.acl-long)

Challenge:	Existing work on integrating audio encoders with large language models (LLMs) has focused on semantic understanding tasks, but different tasks may require distinct features that emphasize either semantic or acoustic aspects.
Approach:	They propose to use a prompt-aware mixture to enhance the Speech LLM that uses multiple audio encoders to extract different features based on the prompt.
Outcome:	The proposed approach outperforms all single-encoder Speech LLMs on ASR, speaker number verification, and AC tasks.

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Tong Zheng, Bei Li, Huiwen Bao, Tong Xiao, JingBo Zhu

Evolutionary Guided Decoding: Iterative Value Refinement for LLMs (2026.acl-long)

Challenge:	Existing multi-view learning models prioritize complementarity while ignoring consensus . EMHA allows for efficient modeling of global dependencies among tokens in parallel .
Approach:	They propose an enhanced multi-head self-attention (EMHA) that prioritizes complementarity while ignoring consensus.
Outcome:	The proposed method favors consensus among heads by introducing two models . it is superior on a wide range of language tasks with a modest increase in model size .

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Zhenhua Liu, Lijun Li, Ruizhe Chen, Yuxian Jiang, Tong Zhu, Zhaochen Su, Wenliang Chen, Jing Shao

RankPrompt: Step-by-Step Comparisons Make Language Models Better Reasoners (2024.lrec-main)

Challenge:	Existing methods for directing language model outputs are limited in their accuracy due to a distributional gap . existing methods train static value functions on trajectories sampled exclusively from the base policy .
Approach:	They propose a framework to bridge a distributional gap in the accuracy of value functions . they propose RLHF to align language models with human values and task requirements .
Outcome:	The proposed framework reduces computational costs and improves value function accuracy by leveraging principled value function optimization.

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Chi Hu, Yuan Ge, Xiangnan Ma, Hang Cao, Qiang Li, Yonghua Yang, Tong Xiao, Jingbo Zhu

Dialogue Systems & Conversational Agents

Challenge:	Existing solutions to reasoning tasks require extensive human annotations or fail in scenarios with inconsistent responses.
Approach:	They propose a new method that enables LLMs to self-rank their responses without additional resources.
Outcome:	The proposed method improves reasoning performance of ChatGPT and GPT-4 with 13% improvement over existing methods.

Improving Relation Extraction with Relational Paraphrase Sentences (2020.coling-main)

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Junjie Yu, Tong Zhu, Wenliang Chen, Wei Zhang, Min Zhang

LangSuit·E: Planning, Controlling and Interacting with Large Language Models in Embodied Text Environments (2024.findings-acl)

Challenge:	Existing annotated data is expensive and non-scalable, limiting performance of relation extraction models.
Approach:	They propose to enrich relation expressions by relational paraphrase sentences by annotating human-annotated data.
Outcome:	The proposed model improves performance even on a strong baseline.

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Zixia Jia, Mengmeng Wang, Baichen Tong, Song-Chun Zhu, Zilong Zheng

Mitigating Hallucinations of Large Language Models in Medical Information Extraction via Contrastive Decoding (2024.findings-emnlp)

Challenge:	Recent advances in Large Language Models (LLMs) have shown inspiring achievements in constructing autonomous agents that rely on language descriptions as inputs.
Approach:	They propose a flexible and simulation-free testbed that simulates 6 representative embodied tasks in textual embodies.
Outcome:	The proposed testbed offers adaptability to diverse environments without multiple simulation engines and allows easy customization of communication and action strategies.

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Derong Xu, Ziheng Zhang, Zhihong Zhu, Zhenxi Lin, Qidong Liu, Xian Wu, Tong Xu, Xiangyu Zhao, Yefeng Zheng, Enhong Chen

Beyond Decoder-only: Large Language Models Can be Good Encoders for Machine Translation (2025.findings-acl)

Challenge:	Medical Information Extraction (MIE) tasks are a fundamental component of medical NLP.
Approach:	They propose an alternative adaptive constraint strategy to adjust the scale and scope of contrastive tokens.
Outcome:	The proposed approach selectively enhances the identification and classification capabilities while minimizing the influence of other inherent abilities in LLMs.

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Yingfeng Luo, Tong Zheng, Yongyu Mu, Bei Li, Qinghong Zhang, Yongqi Gao, Ziqiang Xu, Peinan Feng, Xiaoqian Liu, Tong Xiao, JingBo Zhu

OpenGenAlign: A Preference Dataset and Benchmark for Trustworthy Reward Modeling in Open-Ended, Long-Context Generation (2026.findings-acl)

Challenge:	Recent advances in machine translation have focused on a single pre-trained decoder . encoder-decoder architectures have received relatively little attention in NMT .
Approach:	They propose a method that leverages LLMs as MT encoders and pairs them with lightweight decoders to develop universal translation models.
Outcome:	The proposed method matches or surpasses baselines in terms of translation quality but achieves 75% reduction in memory footprint of the KV cache.

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Hanning Zhang, Juntong Song, Juno Zhu, Yuanhao Wu, Tong Zhang, Cheng Niu

Revisiting Interpolation Augmentation for Speech-to-Text Generation (2024.findings-acl)

Challenge:	Existing reward models perform suboptimal on held-out benchmarks, resulting in poor quality outputs.
Approach:	They propose a framework and a high-quality dataset to evaluate reward models . they define four key metrics to assess generation quality and develop a pipeline to evaluate outputs .
Outcome:	The proposed framework and dataset improves hallucination-free, comprehensive, reliable, and efficient open-ended long-context generation.

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Chen Xu, Jie Wang, Xiaoqian Liu, Qian Dong, Chunliang Zhang, Tong Xiao, JingBo Zhu, Dapeng Man, Wu Yang

Bridging the Granularity Gap for Acoustic Modeling (2023.findings-acl)

Challenge:	Existing approaches to speech-to-text generation tasks are limited by the lack of extensive labeled datasets.
Approach:	They propose to use interpolation augmentation to construct virtual training samples by transforming inputs and labels to enhance generalization in other domains.
Outcome:	The proposed approach significantly improves performance across diverse tasks, architectures, and data scales, offering a promising avenue for more robust S2T systems in resource-constrained settings.

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Chen Xu, Yuhao Zhang, Chengbo Jiao, Xiaoqian Liu, Chi Hu, Xin Zeng, Tong Xiao, Anxiang Ma, Huizhen Wang, Jingbo Zhu

Does Multi-Encoder Help? A Case Study on Context-Aware Neural Machine Translation (2020.acl-main)

Challenge:	Despite the success of speech recognition, how to encode the speech features effectively remains an open problem.
Approach:	They propose a Progressive Down-Sampling technique which compresses acoustic features into coarser-grained units containing more complete semantic information, like text-level representation.
Outcome:	The proposed method yields comparable or better results on the speech recognition task and inference speedups ranging from 1.20x to 1.47x.

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Bei Li, Hui Liu, Ziyang Wang, Yufan Jiang, Tong Xiao, Jingbo Zhu, Tongran Liu, Changliang Li

ClinicalMC: A Benchmark for Multi-Course Clinical Decision-Making with Large Language Models (2026.findings-acl)

Challenge:	In encoder-decoder neural models, multiple encoders are used to represent contextual information in addition to the individual sentence.
Approach:	They propose to use multiple context encoders to encode the individual sentences in document-level neural machine translation (NMT) They propose a noisy dropout setup and a single-encoder approach to encode context sentences.
Outcome:	The proposed approach encodes the context and the current sentence without contexts.

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Ruihui Hou, Siyi Zhu, Ziyue Huai, Guangya Yu, Yongqi Fan, ChunMing Wang, Tong Ruan

RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models (2024.acl-long)

Challenge:	Existing benchmarks assess LLM performance in single-course settings and lack systematic evaluation in multi-course scenarios, where a patient’s condition evolves over time.
Approach:	They propose to use large language models to assess their performance in multi-course clinical decision-making scenarios where a patient’s condition evolves over time.
Outcome:	The proposed model includes 1,275 Chinese and 5,804 English samples across four stages from admission to discharge.

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Cheng Niu, Yuanhao Wu, Juno Zhu, Siliang Xu, KaShun Shum, Randy Zhong, Juntong Song, Tong Zhang

Revealing the Parallel Multilingual Learning within Large Language Models (2024.emnlp-main)

Challenge:	Retrieval-augmented generation (RAG) is a main technique for alleviating hallucinations in large language models.
Approach:	They propose to integrate RAG into large language models to analyze word-level hallucinations using a corpus of 18,000 naturally generated responses from diverse LLMs.
Outcome:	The proposed model can fine tune a relatively small LLM and achieve a competitive hallucination detection performance when compared to the existing prompt-based approaches.

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Yongyu Mu, Peinan Feng, Zhiquan Cao, Yuzhang Wu, Bei Li, Chenglong Wang, Tong Xiao, Kai Song, Tongran Liu, Chunliang Zhang, JingBo Zhu

MobileNMT: Enabling Translation in 15MB and 30ms (2023.acl-industry)

Challenge:	Large language models (LLMs) can handle multilingual and cross-lingual text within a single input; however, previous studies focusing on using English as the pivot language to enhance language understanding and reasoning focus on using multiple languages.
Approach:	They propose to use parallel multilingual input to enhance the model's comprehension of the input and to examine how multilingual processing affects prediction.
Outcome:	The proposed model can handle multilingual and cross-lingual text within a single input, but previous studies focused on using English as the pivot language to enhance language understanding and reasoning.

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Ye Lin, Xiaohui Wang, Zhexi Zhang, Mingxuan Wang, Tong Xiao, Jingbo Zhu

Hybrid Alignment Training for Large Language Models (2024.findings-acl)

Challenge:	Existing work on NMT models is limited in storage, memory, computation and power consumption.
Approach:	They propose a mobile machine translation system that can translate in 15MB and 30ms on devices.
Outcome:	The proposed system can translate in 15MB and 30ms on mobile devices.

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Chenglong Wang, Hang Zhou, Kaiyan Chang, Bei Li, Yongyu Mu, Tong Xiao, Tongran Liu, JingBo Zhu

SERM: Self-Evolving Relevance Model with Agent-Driven Learning from Massive Query Streams (2026.findings-acl)

Challenge:	Existing approaches to align large language models with instructions and preferences are conflicting . et al., 2023b) show that hybrid alignment training can outperform baselines .
Approach:	They propose a hybrid alignment training approach based on alternating alignment and modified elastic weight consolidation methods to achieve better collaboration between different alignment tasks.
Outcome:	The proposed approach outperforms baseline alignment training methods on summarization and dialogue tasks.

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Chenglong Wang, Canjia Li, Xingzhao Zhu, Yifu Huo, Huiyu Wang, Weixiong Lin, Yun Yang, Qiaozhi He, Tian Hua Zhou, null Changxiaojia, JingBo Zhu, Tong Xiao

Challenge:	Existing approaches to generate relevance judgments are limited due to dynamic nature of query distributions.
Approach:	They propose a self-evolving relevance model approach to generalize queries to practical search scenarios . they use a multi-agent sample miner and a relevance annotator to generate reliable labels .
Outcome:	The proposed approach can achieve significant performance gains on a large-scale industrial platform, validated by offline multilingual evaluations and online testing.

Marco-Bench-MIF: On Multilingual Instruction-Following Capability of Large Language (2025.acl-long)

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Bo Zeng, Chenyang Lyu, Sinuo Liu, Mingyan Zeng, Minghao Wu, Xuanfan Ni, Tianqi Shi, Yu Zhao, Yefeng Liu, Chenyu Zhu, Ruizhe Li, Jiahui Geng, Qing Li, Yu Tong, Longyue Wang, Weihua Luo, Kaifu Zhang

Incorporating Probing Signals into Multimodal Machine Translation via Visual Question-Answering Pairs (2023.findings-emnlp)

Challenge:	Existing datasets for instruction-following are monolingual and centered on English . existing data are unable to capture linguistic and cultural subtle differences .
Approach:	They propose an extension of IFEval to a localized multilingual version called Marco-Bench-MIF . their benchmark addresses linguistic constraints and cultural references via translation and verification .
Outcome:	The proposed extension of IFEval to a localized multilingual version covers 30 languages with varying levels of localization.

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Yuxin Zuo, Bei Li, Chuanhao Lv, Tong Zheng, Tong Xiao, JingBo Zhu

BIASEDTALES-ML: A Multilingual Dataset for Analyzing Narrative Attribute Distributions in LLM-Generated Stories (2026.findings-acl)

Challenge:	Existing studies show that multimodal machine translation systems exhibit decreased sensitivity to visual information when text inputs are complete.
Approach:	They propose to generate parallel VQA style pairs from source text to foster more robust cross-modal interaction.
Outcome:	The proposed approach generates parallel VQA style pairs from the source text, fostering more robust cross-modal interaction.

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Yuxuan Ouyang, Yingfeng Luo, JingBo Zhu, Tong Xiao

Dynamic Curriculum Learning for Low-Resource Neural Machine Translation (2020.coling-main)

Challenge:	Existing studies on the use of Large Language Models (LLMs) focus primarily on English, leaving the cross-lingual generalization of aligned behavior underexplored.
Approach:	They propose a structured generator-extractor pipeline and a multi-dimensional distributional analysis framework to examine how narrative attributes vary across languages, models, and social conditions.
Outcome:	The proposed model reveals substantial cross-lingual variability in narrative generation patterns, indicating that distributions observed in English do not always exhibit similar characteristics in other languages, particularly in lower-resource settings.

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Chen Xu, Bojie Hu, Yufan Jiang, Kai Feng, Zeyang Wang, Shen Huang, Qi Ju, Tong Xiao, Jingbo Zhu

Mem-Gallery: Benchmarking Multimodal Long-Term Conversational Memory for MLLM Agents (2026.acl-long)

Challenge:	Recent work on neural machine translation (NMT) has demonstrated impressive performance improvements and became the de-facto standard.
Approach:	They propose a dynamic curriculum learning method to reorder training samples in training using a Transformer-based system.
Outcome:	The proposed method outperforms baselines on three low-resource machine translation benchmarks and different sized data of WMT’16 En-De.

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Yuanchen Bei, Tianxin Wei, Xuying Ning, Yanjun Zhao, Zhining Liu, Xiao Lin, Yada Zhu, Hendrik Hamann, Jingrui He, Hanghang Tong

Natural Language Interfaces

Challenge:	Existing benchmarks evaluate multi-session memory in text-only conversations or assess multimodal understanding within localized contexts.
Approach:	They propose a benchmark for evaluating multimodal long-term conversational memory in MLLM agents.
Outcome:	The proposed framework assesses key memory capabilities along three functional dimensions: memory extraction and test-time adaptation, memory reasoning, and memory knowledge management.

Clustering and Ranking: Diversity-preserved Instruction Selection through Expert-aligned Quality Estimation (2024.emnlp-main)

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Yuan Ge, Yilun Liu, Chi Hu, Weibin Meng, Shimin Tao, Xiaofeng Zhao, Mahong Xia, Zhang Li, Boxing Chen, Hao Yang, Bei Li, Tong Xiao, JingBo Zhu

Bypassing Neural Evaluations for Fast Audio Editing via Adaptive Trajectory Extrapolation (2026.findings-acl)

Challenge:	Existing methods for instruction data selection have limitations such as relying on fragile external APIs, being affected by biases in GPT models, or reducing the diversity of the selected instruction dataset.
Approach:	They propose an industrial-friendly, expert-aligned and diversity-preserved instruction data selection method: Clustering and Ranking (CaR).
Outcome:	The proposed method outperforms Alpaca's existing methods by 32.1% in GPT-4 evaluations.

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Xiaoqian Liu, Zhengkun Ge, Jianjin Wang, Haoran Zhang, Yuan Ge, Kaiyan Chang, Chen Xu, Tong Xiao, Zhengtao Yu, Linfeng Zhang, JingBo Zhu

RankNAS: Efficient Neural Architecture Search by Pairwise Ranking (2021.emnlp-main)

Challenge:	Recent advances in audio diffusion models have significantly improved text-to-audio editing via inversion techniques, but these models typically rely on dense, fixed-step sampling trajectories to maintain structural integrity.
Approach:	They propose a model-agnostic Adaptive Trajectory Extrapolation framework that accelerates inversion-based editing process by dynamically evaluating only the most critical generative phases.
Outcome:	The proposed framework achieves a 3.9 speedup with negligible loss in fidelity.

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Chi Hu, Chenglong Wang, Xiangnan Ma, Xia Meng, Yinqiao Li, Tong Xiao, Jingbo Zhu, Changliang Li

Green & Sustainable NLP

Challenge:	Existing methods require training millions of architectures to estimate the accuracy of the search results.
Approach:	They propose a performance ranking method (RankNAS) that uses pairwise ranking and search space pruning to enlarge the search space.
Outcome:	The proposed method significantly accelerates NAS through pairwise ranking and search space pruning.

On the Emotion Understanding of Synthesized Speech (2026.acl-long)

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Yuan Ge, Haishu Zhao, AoKai Hao, Junxiang Zhang, Bei Li, Xiaoqian Liu, Chenglong Wang, Jianjin Wang, Bingsen Zhou, Bingyu Liu, JingBo Zhu, Zhengtao Yu, Tong Xiao

SLAM: Towards Efficient Multilingual Reasoning via Selective Language Alignment (2025.coling-main)

Challenge:	Existing models for emotion understanding do not capture fundamental features of synthesized speech.
Approach:	They evaluate emotion recognition models on synthesized speech using SER models and generative models.
Outcome:	The proposed model can't generalize to synthesized speech because of speech token prediction . generative models tend to infer emotion from textual semantics while ignoring paralinguistic cues.

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Yuchun Fan, Yongyu Mu, YiLin Wang, Lei Huang, Junhao Ruan, Bei Li, Tong Xiao, Shujian Huang, Xiaocheng Feng, Jingbo Zhu

HIT: Nested Named Entity Recognition via Head-Tail Pair and Token Interaction (2020.emnlp-main)

Challenge:	Large language models (LLMs) have demonstrated significant improvements in reasoning abilities, but these improvements are primarily focused on English, leading to inferior performance in non-English scenarios.
Approach:	They propose a multilingual reasoning alignment approach that fine-tunes the layers responsible for multilingual comprehension in one stage.
Outcome:	The proposed method fine-tunes 6 of the 9 layers responsible for multilingual comprehension, while reducing training time by 4.1-11.9 compared to the two-stage method.

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Yu Wang, Yun Li, Hanghang Tong, Ziye Zhu

CTC-based Non-autoregressive Speech Translation (2023.acl-long)

Challenge:	Named Entity Recognition (NER) is a fundamental task in natural language processing due to the nature of the named entity.
Approach:	They propose a nested NER model that leverages two key properties pertaining to the named entity, including explicit boundary tokens and tight internal connection between tokens within the boundary.
Outcome:	The proposed model achieves state-of-the-art on three public NER datasets.

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Chen Xu, Xiaoqian Liu, Xiaowen Liu, Qingxuan Sun, Yuhao Zhang, Murun Yang, Qianqian Dong, Tom Ko, Mingxuan Wang, Tong Xiao, Anxiang Ma, Jingbo Zhu

TranSFormer: Slow-Fast Transformer for Machine Translation (2023.findings-acl)

Challenge:	End-to-end speech translation (E2E ST) and non-autoregressive (NAR) generation are promising in language and speech processing for their advantages of less error propagation and low latency.
Approach:	They develop a model that uses connectionist temporal classification to predict the source and target texts.
Outcome:	The proposed model achieves an average BLEU score of 29.5 with a speed-up of 5.67.

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Bei Li, Yi Jing, Xu Tan, Zhen Xing, Tong Xiao, Jingbo Zhu

Exploiting Target Language Data for Neural Machine Translation Beyond Back Translation (2024.findings-acl)

Challenge:	Prior work has focused on treating subwords as basic units in developing such systems.
Approach:	They propose a slow-fast two-stream learning model that uses a “slow” branch to deal with subword sequences and a "fast" branch to cope with longer character sequences.
Outcome:	The proposed model shows consistent BLEU improvements (larger than 1 BLUE point) on several machine translation benchmarks.

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Abudurexiti Reheman, Yingfeng Luo, Junhao Ruan, Chunliang Zhang, Anxiang Ma, Tong Xiao, JingBo Zhu

ODE Transformer: An Ordinary Differential Equation-Inspired Model for Sequence Generation (2022.acl-long)

Challenge:	Neural Machine Translation (NMT) encounters challenges when translating in new domains and low-resource languages.
Approach:	They propose a variant of k-nearest neighbor machine translation that utilizes target language data by constructing a pseudo datastore.
Outcome:	The proposed method exhibits strong domain adaptation capability in both high-resource and low-resourced machine translation.

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Bei Li, Quan Du, Tao Zhou, Yi Jing, Shuhan Zhou, Xin Zeng, Tong Xiao, JingBo Zhu, Xuebo Liu, Min Zhang

FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine Translation (2022.lrec-1)

Challenge:	Residual networks are an Euler discretization of solutions to Ordinary Differential Equations (ODE).
Approach:	They propose a residual block of layers in Transformer that can be described as a higher-order solution to ODE.
Outcome:	The proposed architecture can gain large improvements over strong baselines at a slight cost in inference efficiency.

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Wenhao Zhu, Shujian Huang, Tong Pu, Pingxuan Huang, Xu Zhang, Jian Yu, Wei Chen, Yanfeng Wang, Jiajun Chen

HEAL: A Hypothesis-Based Preference-Aware Analysis Framework (2025.findings-emnlp)

Challenge:	Recent research on domain adaptation neglects diversity in translation within a domain . current research on NMT models considers very broad target domains .
Approach:	They propose a fine-grained domain adaptation task for autonomous vehicles, AI education, real-time networks, and smart phone.
Outcome:	The proposed task is compared with a dataset of Chinese-English translation tasks for four sub-domains of information technology: autonomous vehicles, AI education, real-time networks, and smart phone.

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Yifu Huo, Chenglong Wang, Qiren Zhu, Shunjie Xing, Tong Xiao, Chunliang Zhang, Tongran Liu, JingBo Zhu

Challenge:	Preference optimization methods like DPO are often evaluated on a single response, overlooking other outputs.
Approach:	They propose a Hypothesis-based PrEference-aware AnaLysis Framework that formulates preference alignment as a re-ranking process within hypothesis spaces.
Outcome:	The proposed evaluation paradigm re-ranks preference alignment as a reranking process within hypothesis spaces.

Position IDs Matter: An Enhanced Position Layout for Efficient Context Compression in Large Language Models (2025.findings-emnlp)

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Runsong Zhao, Xin Liu, Xinyu Liu, Pengcheng Huang, Chunyang Xiao, Tong Xiao, JingBo Zhu

Stacked Acoustic-and-Textual Encoding: Integrating the Pre-trained Models into Speech Translation Encoders (2021.acl-long)

Challenge:	Existing methods to compress context information ignore holistic contextual dependencies.
Approach:	They propose a method that adjusts position encodings to minimize the distance between context tokens and special tokens.
Outcome:	Enhanced Position Layout (EPL) improves compression of context information in large language models.

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Chen Xu, Bojie Hu, Yanyang Li, Yuhao Zhang, Shen Huang, Qi Ju, Tong Xiao, Jingbo Zhu

MTR-Suite: A Framework for Evaluating and Synthesizing Conversational Retrieval Benchmarks (2026.acl-long)

Challenge:	End-to-end Speech Translation (E2E ST) encoders lack global context representation, whereas MT encoder lacks it.
Approach:	They propose a Stacked Acoustic-and-Textual Encoding method for speech translation . they propose an adaptor module to alleviate representation inconsistency .
Outcome:	The proposed method achieves state-of-the-art BLEU scores of 18.3 and 25.2 on two ST tasks.

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Junhao Ruan, Abudukeyumu Abudula, Bei Li, Yongjing Yin, Xinyu Liu, Kechen Jiao, Xin Chen, Jingang Wang, Xunliang Cai, Tong Xiao, JingBo Zhu

Natural Language Interfaces

Challenge:	Existing conversational retrieval benchmarks suffer from costly, sparse human annotation or rigid, unnatural automated heuristics.
Approach:	They propose a framework for auditing, synthesizing, and benchmarking conversational retrieval.
Outcome:	The proposed framework is based on three LLM-based auditors and a multi-agent system . it mimics production-style challenges (hard topic switching, verbosity) and offers superior discriminative power.

IIET: Efficient Numerical Transformer via Implicit Iterative Euler Method (2025.emnlp-main)

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Xinyu Liu, Bei Li, Jiahao Liu, Junhao Ruan, Kechen Jiao, Hongyin Tang, Jingang Wang, Tong Xiao, JingBo Zhu

Green & Sustainable NLP

Challenge:	High-order numerical methods enhance performance in tasks like NLP but introduce a performance-efficiency trade-off due to increased computational overhead.
Approach:	They propose an iterative implicit Euler Transformer which simplifies high-order numerical methods by iterating implicit Eule.
Outcome:	The proposed method improves accuracy and reduces inference overhead by 55% while retaining 99.4% of the original task accuracy.

Improving Autoregressive Grammatical Error Correction with Non-autoregressive Models (2023.findings-acl)

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Hang Cao, Zhiquan Cao, Chi Hu, Baoyu Hou, Tong Xiao, Jingbo Zhu

Syntactic Text Processing

Challenge:	Autoregressive models assign low probabilities to tokens that need corrections . grammatical error correction (GEC) is widely applied to natural language processing tasks .
Approach:	They propose to use a non-autoregressive model as an auxiliary model to train GEC models to correct grammatical errors in sentences.
Outcome:	The proposed method outperforms baselines on English and Chinese GEC tasks significantly.

Living in the Moment: Can Large Language Models Grasp Co-Temporal Reasoning? (2024.acl-long)

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Zhaochen Su, Juntao Li, Jun Zhang, Tong Zhu, Xiaoye Qu, Pan Zhou, Yan Bowen, Yu Cheng, Min Zhang

Improved Knowledge Distillation for Pre-trained Language Models via Knowledge Selection (2022.findings-emnlp)

Challenge:	Current temporal reasoning datasets are limited to questions about single or isolated events, falling short in mirroring the realistic temporal characteristics involving concurrent nature and intricate temporal interconnections.
Approach:	They propose a co-temporal Question Answering benchmark that contains four co-time scenarios with 4,748 samples for evaluating the co-timing abilities of large language models.
Outcome:	The proposed benchmarks show that current LLMs struggle on CoTempQA tasks even when enhanced with Chain of Thought methodologies.

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Chenglong Wang, Yi Lu, Yongyu Mu, Yimin Hu, Tong Xiao, Jingbo Zhu

Language-Specific Layer Matters: Efficient Multilingual Enhancement for Large Vision-Language Models (2025.findings-emnlp)

Challenge:	Existing studies on knowledge distillation have shown that not all knowledge is necessary for learning a good student model.
Approach:	They propose an actor-critic approach to selecting appropriate knowledge to transfer during the process of knowledge distillation.
Outcome:	The proposed method outperforms several strong knowledge distillation baselines significantly on the GLUE datasets.

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Yuchun Fan, Yilin Wang, Yongyu Mu, Lei Huang, Bei Li, Xiaocheng Feng, Tong Xiao, JingBo Zhu

Mirror: A Universal Framework for Various Information Extraction Tasks (2023.emnlp-main)

Challenge:	Large vision-language models exhibit an imbalance in multilingual capabilities .
Approach:	They propose a training recipe that achieves efficient multilingual enhancement for LVLMs by Precise Language Specific layers fine-tuning.
Outcome:	The proposed training recipe achieves efficient multilingual enhancement for LVLMs by fine-tuning language specific layers.

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Tong Zhu, Junfei Ren, Zijian Yu, Mengsong Wu, Guoliang Zhang, Xiaoye Qu, Wenliang Chen, Zhefeng Wang, Baoxing Huai, Min Zhang

Forgetting Curve: A Reliable Method for Evaluating Memorization Capability for Long-Context Models (2024.emnlp-main)

Challenge:	Recent studies often formulate IE tasks as a triplet extraction problem, but this paradigm does not support multi-span and n-ary extraction, leading to weak versatility.
Approach:	They propose a multi-span cyclic graph extraction problem and a non-autoregressive graph decoding algorithm to extract all spans in a single step.
Outcome:	The proposed model outperforms or reaches competitive performance with SOTA systems under few-shot and zero-shot settings and it is compatible with 57 datasets.

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Xinyu Liu, Runsong Zhao, Pengcheng Huang, Chunyang Xiao, Bei Li, Jingang Wang, Tong Xiao, JingBo Zhu

Multi-layer Representation Fusion for Neural Machine Translation (C18-1)

Challenge:	Existing methods, tasks and benchmarks to measure model’s effective memory length are limited.
Approach:	They propose a method called forgetting curve to measure the memorization capability of long-context models.
Outcome:	The proposed method is robust to the tested corpus and experimental settings, and can be applied to any model size.

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Qiang Wang, Fuxue Li, Tong Xiao, Yanyang Li, Yinqiao Li, Jingbo Zhu

DocEE: A Large-Scale and Fine-grained Benchmark for Document-level Event Extraction (2022.naacl-main)

Challenge:	Neural machine translation systems require a number of stacked layers for deep models, but the prediction depends on the sentence representation of the top-most layer with no access to low-level representations.
Approach:	They propose a multi-layer representation fusion approach to fusing stacked layers to learn a better representation from the stack.
Outcome:	The proposed approach yields 0.92 and 0.56 BLEU points over the strong Transformer baseline on IWSLT German-English and NIST Chinese-English MT tasks respectively.

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MeiHan Tong, Bin Xu, Shuai Wang, Meihuan Han, Yixin Cao, Jiangqi Zhu, Siyu Chen, Lei Hou, Juanzi Li

Sentence-Permuted Paragraph Generation (2021.emnlp-main)

Challenge:	Existing datasets focus on sentence-level event extraction, but document-level EE is limited due to the lack of large-scale and practical training and evaluation datasets.
Approach:	They propose a document-level event extraction dataset with 27,000+ events and 180,000+ arguments.
Outcome:	The proposed dataset includes 27,000+ events, 180,000+ arguments and large-scale manual annotations, fine-grained argument types and application-oriented settings.

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Wenhao Yu, Chenguang Zhu, Tong Zhao, Zhichun Guo, Meng Jiang

VeraCT Scan: Retrieval-Augmented Fake News Detection with Justifiable Reasoning (2024.acl-demos)

Challenge:	Existing models produce similar contents from homogenized contexts due to the fixed left-to-right sentence order.
Approach:	They propose a framework permuting sentence orders to improve content diversity of multi-sentence paragraphs by permutating the sentence orders.
Outcome:	The proposed framework produces more diverse outputs with higher quality than existing models.

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Cheng Niu, Yang Guan, Yuanhao Wu, Juno Zhu, Juntong Song, Randy Zhong, Kaihua Zhu, Siliang Xu, Shizhe Diao, Tong Zhang

Learning to Refuse: Towards Mitigating Privacy Risks in LLMs (2025.coling-main)

Challenge:	generative artificial intelligence has exacerbated the challenge of distinguishing genuine news from fabricated stories.
Approach:	They propose a retrieval-augmented system that extracts the core facts from a given piece of news and conducts an internet-wide search to identify corroborating or conflicting reports.
Outcome:	The proposed system has demonstrated state-of-the-art accuracy in the realm of fake news detection.

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Zhenhua Liu, Tong Zhu, Chuanyuan Tan, Wenliang Chen

PartialFormer: Modeling Part Instead of Whole for Machine Translation (2024.findings-acl)

Challenge:	Large language models (LLMs) exhibit remarkable capabilities in understanding and generating natural languages, but can inadvertently memorize private information, posing significant privacy risks.
Approach:	They propose to use a dataset to evaluate machine unlearning methods for protecting personal data in a realistic scenario.
Outcome:	The proposed model outperforms baseline methods by 5.65 points and protects target individuals’ personal data while maintaining general capabilities.

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Tong Zheng, Bei Li, Huiwen Bao, Jiale Wang, Weiqiao Shan, Tong Xiao, JingBo Zhu

Learning Architectures from an Extended Search Space for Language Modeling (2020.acl-main)

Challenge:	Existing feed-forward neural networks have significant computational and parametric overhead.
Approach:	They propose a parameter-efficient Transformer architecture that utilizes multiple smaller FFNs to reduce parameters and computation while maintaining essential hidden dimensions.
Outcome:	The proposed architecture reduces computational and parameter overhead while maintaining essential hidden dimensions.

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Yinqiao Li, Chi Hu, Yuhao Zhang, Nuo Xu, Yufan Jiang, Tong Xiao, Jingbo Zhu, Tongran Liu, Changliang Li

MoPE: Mixture of Prefix Experts for Zero-Shot Dialogue State Tracking (2024.lrec-main)

Challenge:	Neural architecture search (NAS) has advanced in recent years but most NAS systems restrict search to learning architectures of a recurrent or convolutional cell.
Approach:	They propose a general approach to learn both intra-cell and inter-cell architectures . they implement their approach in a differentiable architecture search system .
Outcome:	The proposed approach outperforms the baseline on PTB and WikiText data and shows good transferability to other systems.

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Tianwen Tang, Tong Zhu, Haodong Liu, Yin Bai, Jia Cheng, Wenliang Chen

CoMeT: Collaborative Memory Transformer for Efficient Long Context Modeling (2026.acl-long)

Challenge:	Existing zero-shot dialogue state tracking models suffer from domain transferring and partial prediction problems.
Approach:	They propose to establish connections between similar slots in different domains to improve model transfer performance in unseen domains.
Outcome:	Empirical results show that the proposed model achieves the goal accuracy of 57.13% on MultiWOZ2.1 and 55.4.

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Runsong Zhao, Shilei Liu, Jiwei Tang, Langming Liu, Haibin Chen, Weidong Zhang, Yujin Yuan, Tong Xiao, JingBo Zhu, Wenbo Su, Bo Zheng

Training Flexible Depth Model by Multi-Task Learning for Neural Machine Translation (2020.findings-emnlp)

Challenge:	a novel architecture that enables LLMs to handle arbitrarily long sequences with constant memory usage and linear time complexity is a major barrier to long-context processing.
Approach:	They propose a novel architecture that enables LLMs to handle arbitrarily long sequences with constant memory usage and linear time complexity.
Outcome:	The proposed architecture can handle arbitrarily long sequences with constant memory usage and linear time complexity.

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Qiang Wang, Tong Xiao, Jingbo Zhu

Augmenting Large Language Model Translators via Translation Memories (2023.findings-acl)

Challenge:	Experimental results show that multitask learning can support decoding in 24 depth configurations and is superior to individual training.
Approach:	They propose to use multi-task learning to train a flexible depth model that can adapt to different depth configurations during inference.
Outcome:	The proposed model can support decoding in 24 depth configurations and is superior to the individual training and another flexible depth model training method——LayerDrop.

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Yongyu Mu, Abudurexiti Reheman, Zhiquan Cao, Yuchun Fan, Bei Li, Yinqiao Li, Tong Xiao, Chunliang Zhang, Jingbo Zhu

SPS: Steering Probability Squeezing for Better Exploration in Reinforcement Learning for Large Language Models (2026.findings-acl)

Challenge:	Using translation memories (TMs) as prompts is a promising approach to in-context learning of machine translation models.
Approach:	They propose to use translation memories (TMs) as prompts to prompt large language models (LLMs) they find that the ability of LLMs to "understand" prompts is helpful .
Outcome:	The results are comparable to state-of-the-art NMT systems with bilingual data and are tuned on downstream tasks.

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Yifu Huo, Chenglong Wang, Ziming Zhu, Shunjie Xing, Peinan Feng, Tongran Liu, Qiaozhi He, Tian Hua Zhou, null Changxiaojia, JingBo Zhu, Zhengtao Yu, Tong Xiao

Layer-Wise Multi-View Learning for Neural Machine Translation (2020.coling-main)

Challenge:	Reinforcement learning (RL) training typically improves single-sample success rates but limited exploration of diverse reasoning trajectories.
Approach:	They propose a training paradigm that interleaves conventional RL with inverse reinforcement learning (IRL) they propose 'Steering Probability Squeezing' to enhance exploration without external supervision .
Outcome:	The proposed training paradigm improves Pass@k and improves exploration of diverse reasoning trajectories without external supervision.

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Qiang Wang, Changliang Li, Yue Zhang, Tong Xiao, Jingbo Zhu

A Simple and Effective Approach to Coverage-Aware Neural Machine Translation (P18-2)

Challenge:	Existing approaches to neural machine translation are limited to the topmost encoder layer’s context representation and cannot perceive the lower encoder layers.
Approach:	They propose a layer-wise multi-view learning approach to solve this problem by incorporating an auxiliary view into the model.
Outcome:	The proposed model can achieve stable results over multiple strong baselines and is agnostic to network architectures.

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Yanyang Li, Tong Xiao, Yinqiao Li, Qiang Wang, Changming Xu, Jingbo Zhu

SEOE: A Scalable and Reliable Semantic Evaluation Framework for Open Domain Event Detection (2025.acl-long)

Challenge:	Neural Machine Translation (NMT) models are used to solve translation problems using long-term models.
Approach:	They propose a method to seek a better balance between model confidence and length preference for Neural Machine Translation.
Outcome:	The proposed model improves on Chinese-English and English-German translation tasks.

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Yi-Fan Lu, Xian-Ling Mao, Tian Lan, Tong Zhang, Yu-Shi Zhu, Heyan Huang

NesTools: A Dataset for Evaluating Nested Tool Learning Abilities of Large Language Models (2025.coling-main)

Challenge:	Existing evaluation methods for Open Domain Event Detection (ODED) lack representative representations of the real world, making it difficult to accurately reflect performance of various ODED methods in real-world scenarios.
Approach:	They propose a scalable and reliable Semantic-level Evaluation framework for Open domain event detection by constructing a more representative evaluation benchmark and introducing a semantic evaluation metric.
Outcome:	The proposed framework first constructs a more representative evaluation benchmark that currently includes 564 event types covering 7 major domains, with a cost-effective supplementary annotation strategy to ensure the benchmark’s representativeness.

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Han Han, Tong Zhu, Xiang Zhang, MengSong Wu, Xiong Hao, Wenliang Chen

On Vision Features in Multimodal Machine Translation (2022.acl-long)

Challenge:	Existing benchmarks on nested tool learning are lacking relevant data instances.
Approach:	They propose a method to construct large-scale nested tool calls with different nesting structures using a large-quality dataset.
Outcome:	The proposed method can be used to evaluate the nested tool learning abilities of large language models (LLMs) in real-world applications.

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Bei Li, Chuanhao Lv, Zefan Zhou, Tao Zhou, Tong Xiao, Anxiang Ma, JingBo Zhu

Shallow-to-Deep Training for Neural Machine Translation (2020.emnlp-main)

Challenge:	Recent work on multimodal machine translation (MMT) has focused on the way of incorporating vision features into translation but little attention is given to the quality of vision models.
Approach:	They develop a selective attention model to study the patch-level contribution of an image in multimodal machine translation.
Outcome:	The proposed model is able to learn translation from the visual modality on probing tasks and is compared with existing models.

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Bei Li, Ziyang Wang, Hui Liu, Yufan Jiang, Quan Du, Tong Xiao, Huizhen Wang, Jingbo Zhu

Dynamic Data Mixing Maximizes Instruction Tuning for Mixture-of-Experts (2025.naacl-long)

Challenge:	Experimental results show that deep training is 1:4 faster than training from scratch.
Approach:	They propose a shallow-to-deep training method that learns deep models by stacking shallow models.
Outcome:	The proposed method is 1:4 faster than training from scratch and achieves BLEU scores of 30:33 and 43:29 on two translation tasks.

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Tong Zhu, Daize Dong, Xiaoye Qu, Jiacheng Ruan, Wenliang Chen, Yu Cheng

Enhancing Neural Machine Translation Through Target Language Data: A kNN-LM Approach for Domain Adaptation (2025.acl-long)

Challenge:	Mixture-of-Experts (MoE) models are constrained by their fixed model capacities when the number of tasks grows in instruction tuning.
Approach:	They propose to combine all training tasks and apply fixed sampling weights without considering the importance of different tasks as the model training state changes.
Outcome:	The proposed method can be used on knowledge & reasoning tasks and open-ended queries with limited training budget.

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Abudurexiti Reheman, Hongyu Liu, Junhao Ruan, Abudukeyumu Abudula, Yingfeng Luo, Tong Xiao, JingBo Zhu

MCLE-Mol: Empowering LLM with Molecular Comprehension and Low-Cost Continual Evolution for Interpretable Property Prediction (2026.findings-acl)

Challenge:	Neural machine translation (NMT) has made significant progress in recent years, yet often suffers from translating in new domains, which is called domain adaptation.
Approach:	They propose a method that leverages semantically similar target language sentences in the kNN framework and generates a probability distribution over these sentences during decoding.
Outcome:	The proposed method generates a probability distribution over similar target language sentences and then interpolates with the model’s distribution.

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Zhili Pu, Lantian Zhang, Hao Duan, Zhixing Zhang, Keyun Zhu, Yongqi Fan, Ruihui Hou, Tong Ruan, Yun Tang

Explainability & Interpretability in NLP

Challenge:	Large language models (LLMs) offer a new paradigm for molecular property prediction (MPP), yet a semantic gap between natural language and molecul representations limits their ability to capture structure–activity relationships (SAR).
Approach:	They propose an ML–LLM–Rule collaborative framework for MPP that injects ML-derived substructure attribution values into LLMs and calibrates them under specific chemical contexts.
Outcome:	The proposed framework outperforms baseline models on multiple benchmark datasets and is highly interpretable.

MolSafeEval: A Benchmark for Uncovering Safety Risks in AI-Generated Molecules (2026.findings-acl)

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Tong Xu, Xinzhe Cao, Zhihui Zhu, Keyan Ding, Huajun Chen

Structured Data in NLP

Challenge:	generative models may produce molecules with toxic, reactive, or otherwise hazardous characteristics.
Approach:	They propose a benchmark to evaluate and analyze the safety risks of molecular generation.
Outcome:	The proposed benchmark aims to evaluate and analyze the safety risks of molecular generation.

ATIR: Towards Audio-Text Interleaved Contextual Retrieval (2026.acl-long)

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Tong Zhao, Chenghao Zhang, Yutao Zhu, Zhicheng Dou

Point, Disambiguate and Copy: Incorporating Bilingual Dictionaries for Neural Machine Translation (2021.acl-long)

Challenge:	Recent multimodal information retrieval research has focused on images, largely overlooking audio.
Approach:	They propose an audio-text interleaved contextual retrieval task where queries can alternate between audio and text modalities.
Outcome:	The proposed model significantly improves over baselines.

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Tong Zhang, Long Zhang, Wei Ye, Bo Li, Jinan Sun, Xiaoyu Zhu, Wen Zhao, Shikun Zhang

NiuTrans.LMT: Toward Inclusive and Scalable Multilingual Machine Translation with LLMs (2026.acl-long)

Challenge:	Existing approaches to incorporate bilingual dictionaries into Neural Machine Translation (NMT) models have been criticized for lack of integration of bilingual lexical information into the neural architecture.
Approach:	They propose a neural architecture to incorporate bilingual dictionaries into Neural Machine Translation models by introducing three new components: Pointer, Disambiguator, and Copier.
Outcome:	The proposed method achieves the following merits inherently compared with previous efforts: (1) Pointer leverages the semantic information from bilingual dictionaries, for the first time, to better locate source words whose translation in dictionary can potentially be used; (2) Disambiguator synthesizes contextual information from source view and target view, both of which contribute to distinguishing translation of a specific source word from multiple candidates in dicaries; (3) Copier systematically connects Pointer and Disambiguators based on a hierarchical

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Yingfeng Luo, Ziqiang Xu, Yuxuan Ouyang, MuRun Yang, DingYang Lin, Kaiyan Chang, Tong Zheng, Bei Li, Peinan Feng, Quan Du, Tong Xiao, JingBo Zhu

MRT: Multi-modal Short- and Long-range Temporal Convolutional Network for Time-sync Comment Video Behavior Prediction (2024.lrec-main)

Challenge:	Large language models have significantly advanced Multilingual Machine Translation (MMT) yet scaling to many languages while maintaining robust performance across directions remains challenging.
Approach:	They propose a strategy to reduce the number of translations in one direction . they propose auxiliary parallel sentences to promote cross-lingual transfer .
Outcome:	The proposed model performs on par with or better than substantially larger baselines.

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Weihao Zhao, Weidong He, Hao Wang, Haoyang Bi, Han Wu, Chen Zhu, Tong Xu, Enhong Chen

A Document-Level Neural Machine Translation Model with Dynamic Caching Guided by Theme-Rheme Information (2020.coling-main)

Challenge:	Using time-sync comments, it is difficult to understand user behavior due to complexity of interactions between users, videos, and comments.
Approach:	They propose a novel time-sync comment behavior prediction model that takes historical behavior into account and optimizes it on the basis of user preferences.
Outcome:	The proposed model improves the performance of time-sync comments on visual frames and textual comments on two cats playing simultaneously.

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Yiqi Tong, Jiangbin Zheng, Hongkang Zhu, Yidong Chen, Xiaodong Shi

Teaching Language Models to Self-Improve by Learning from Language Feedback (2024.findings-acl)

Challenge:	Recent studies have shown that inter-sentence information is helpful for improving the performance of document-level Neural Machine Translation models, but what information should be regarded as context remains ambiguous.
Approach:	They propose a cache-based document-level NMT model which conducts dynamic caching guided by theme-rheme information.
Outcome:	The proposed model achieves substantial improvements over the state-of-the-art models on NIST evaluation sets.

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Chi Hu, Yimin Hu, Hang Cao, Tong Xiao, JingBo Zhu

LLaMA-MoE: Building Mixture-of-Experts from LLaMA with Continual Pre-Training (2024.emnlp-main)

Challenge:	Recent advances in Large Language Models (LLMs) generate content that can be untruthful or harmful.
Approach:	They propose a method that leverages model feedback for alignment . they use a base language model to generate initial responses, critiqued and refined .
Outcome:	The proposed method outperforms strong baselines across diverse tasks and model sizes.

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Tong Zhu, Xiaoye Qu, Daize Dong, Jiacheng Ruan, Jingqi Tong, Conghui He, Yu Cheng

Improved Differentiable Architecture Search for Language Modeling and Named Entity Recognition (D19-1)

Challenge:	Mixture-of-Experts (MoE) has gained increasing popularity as a framework for scaling up large language models.
Approach:	They investigate how to build Mixture-of-Experts (MoE) models from existing large language models . they use expert construction, Continual pre-training and data sampling strategies .
Outcome:	The proposed model outperforms existing models with similar parameters on a wide range of tasks.

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Yufan Jiang, Chi Hu, Tong Xiao, Chunliang Zhang, Jingbo Zhu

RouteLMT: Learned Sample Routing for Hybrid LLM Translation Deployment (2026.acl-industry)

Challenge:	Neural architecture search (NAS) is a popular approach for finding new models and freeing researchers from the hard work of designing network architectures.
Approach:	They propose differentiable neural architecture search methods for natural language processing . they remove the softmax-local constraint and apply it to named entity recognition .
Outcome:	The proposed method outperforms strong baselines on the language modeling task.

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Yingfeng Luo, Hongyu Liu, DingYang Lin, Kaiyan Chang, Chenglong Wang, Bei Li, Quan Du, Tong Xiao, JingBo Zhu

Learning Deep Transformer Models for Machine Translation (P19-1)

Challenge:	Existing routing strategies rely on heuristics, external predictors, or absolute quality estimation to capture whether the large model provides a worthwhile improvement over the small one.
Approach:	They propose a budget allocation problem for routing large model to large model . they propose heuristics, external predictors, or absolute quality estimation to determine the optimal signal for budgeted decisions.
Outcome:	The proposed model outperforms heuristics, quality/difficulty estimation baselines and achieves a superior quality–budget Pareto frontier.

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Qiang Wang, Bei Li, Tong Xiao, Jingbo Zhu, Changliang Li, Derek F. Wong, Lidia S. Chao

Rethinking and Improving Multi-task Learning for End-to-end Speech Translation (2023.emnlp-main)

Challenge:	Neural machine translation models have advanced the previous state-of-the-art by learning mappings between sequences via neural networks and attention mechanisms.
Approach:	They propose to use layer normalization to pass the combination of previous layers to the next layer to improve the model.
Outcome:	The proposed model outperforms the shallow Transformer-Big/Base baseline model on English-German and Chinese-English tasks by 0.4-2.4 BLEU points.

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Yuhao Zhang, Chen Xu, Bei Li, Hao Chen, Tong Xiao, Chunliang Zhang, Jingbo Zhu

CLIP-MoE: Towards Building Mixture of Experts for CLIP with Diversified Multiplet Upcycling (2025.emnlp-main)

Challenge:	auxiliary tasks are highly consistent with end-to-end speech translation (ST) but their effectiveness has not been thoroughly studied.
Approach:	They propose an improved multi-task learning approach for the ST task that bridges the modal gap by mitigating the difference in length and representation.
Outcome:	The proposed approach achieves state-of-the-art on the MuST-C dataset with 20.8% of training time required by the current SOTA method.

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Jihai Zhang, Xiaoye Qu, Tong Zhu, Yu Cheng

R1-RE: Cross-Domain Relation Extraction with RLVR (2026.acl-long)

Challenge:	Recent studies found that CLIP can only encode one aspect of the feature space, leading to substantial information loss and indistinctive features.
Approach:	They propose a model-agnostic approach that fine-tunes complementary CLIP models and transforms them into a CLIP-MoE.
Outcome:	The proposed framework fine-tunes a series of complementary CLIP models and transforms them into a CLIP-MoE.

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Runpeng Dai, Tong Zheng, Run Yang, Kaixian Yu, Hongtu Zhu

Probing Language Models for Pre-training Data Detection (2024.acl-long)

Challenge:	Relation extraction (RE) is a core task in natural language processing.
Approach:	They propose a supervised learning task for relation extraction (RE) based on annotation guidelines.
Outcome:	The proposed model achieves an average OOD accuracy of 70%, on par with leading proprietary models such as GPT-4o.

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Zhenhua Liu, Tong Zhu, Chuanyuan Tan, Bing Liu, Haonan Lu, Wenliang Chen

Multi-perspective Improvement of Knowledge Graph Completion with Large Language Models (2024.lrec-main)

Challenge:	Large Language Models (LLMs) have shown impressive capabilities, while raising concerns about the data contamination due to privacy issues and leakage of benchmark datasets in the pre-training phase.
Approach:	They propose to utilize the probing technique to examine the model’s internal activations to detect pre-training data contamination by examining the model's internal activates.
Outcome:	The proposed method outperforms baselines and achieves state-of-the-art performance on both WikiMIA and ArxivMIA, with additional experiments confirming its efficacy.

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Derong Xu, Ziheng Zhang, Zhenxi Lin, Xian Wu, Zhihong Zhu, Tong Xu, Xiangyu Zhao, Yefeng Zheng, Enhong Chen

Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation Extraction (2020.coling-main)

Challenge:	Knowledge graph completion (KGC) is a widely used method to tackle incompleteness in knowledge graphs (KGs).
Approach:	They propose a general framework to compensate for the deficiency of contextualized knowledge by querying large language models from various perspectives.
Outcome:	The proposed framework improves knowledge graph completion (KGC) by querying large language models from various perspectives.

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Tong Zhu, Haitao Wang, Junjie Yu, Xiabing Zhou, Wenliang Chen, Wei Zhang, Min Zhang

LANG: Reinforcement Learning for Multilingual Reasoning with Language-Adaptive Hint Guidance (2026.acl-long)

Challenge:	Distant Supervision (DS) generates large-scale annotated data but has wrong labels that result in incorrect evaluation scores during testing.
Approach:	They build a dataset using DS-generated data as training data and hire annotators to label test data.
Outcome:	The proposed dataset NYTH has a much larger test set and performs more accurate and consistent evaluation.

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Yuchun Fan, Bei Li, Peiguang Li, Yilin Wang, Yongyu Mu, Jian Yang, Xin Chen, Rongxiang Weng, Jingang Wang, Xunliang Cai, JingBo Zhu, Tong Xiao