Papers by Jingjing Li
Cross-media User Profiling with Joint Textual and Social User Embedding (C18-1)
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| Challenge: | Empirical studies demonstrate the effectiveness of the proposed approach to cross-media user profiling tasks. |
| Approach: | They propose a uniform user embedding learning approach to address cross-media user profiling by bridging the knowledge between the source and target media. |
| Outcome: | Empirical results show that the proposed approach performs well on two cross-media user profiling tasks. |
Multimodal Topic-Enriched Auxiliary Learning for Depression Detection (2020.coling-main)
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| Challenge: | Existing studies on depression detection rely on textual and visual content to determine whether a human being is depressed or non-depressed. |
| Approach: | They propose a multimodal topic-enriched Auxiliary Learning approach that captures topic information from texts and images for depression detection. |
| Outcome: | The proposed approach improves the performance of the primary task by using topic information from text and images. |
Coherent Comments Generation for Chinese Articles with a Graph-to-Sequence Model (P19-1)
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| Challenge: | Existing models for article comment generation are too long and often result in general and irrelevant comments. |
| Approach: | They propose to generate comments with a graph-to-sequence model that models the input news as a topic interaction graph. |
| Outcome: | The proposed model can generate coherent and informative comments compared with several strong baseline models. |
Enhancing Auto-regressive Chain-of-Thought through Loop-Aligned Reasoning (2026.eacl-long)
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| Challenge: | Chain-of-Thought prompting is a powerful technique for enhancing language model’s reasoning capabilities, but generating long and correct CoT trajectories is challenging. |
| Approach: | They propose to align the steps of Chain-of-Thought reasoning with loop iterations and apply intermediate supervision during the training of Looped Transformers. |
| Outcome: | The proposed method generates accurate reasoning chains for complex problems exceeding training length, and improves performance of the auto-regressive model. |
LightningDOT: Pre-training Visual-Semantic Embeddings for Real-Time Image-Text Retrieval (2021.naacl-main)
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| Challenge: | Existing pre-trained models suffer from slow inference speed due to cross-modal attention in transformer architecture. |
| Approach: | They propose a multimodal approach that accelerates the inference time of ITR by thousands of times . they extract pre-cached feature indexes offline and employ instant dot-product matching online . |
| Outcome: | The proposed approach outperforms existing models that consume 1000 times magnitude of computational hours using the same features. |
VOLTA: Improving Generative Diversity by Variational Mutual Information Maximizing Autoencoder (2024.findings-naacl)
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| Challenge: | generative diversity is a critical yet underexplored issue in natural language generation . previous approaches to enhance diversity of Transformer models have been limited by their latent variables . |
| Approach: | They propose a framework that bridges Transformer with VAE to enhance generative diversity. |
| Outcome: | The proposed framework improves generative diversity while maintaining generative quality. |
Extrapolating Multilingual Understanding Models as Multilingual Generators (2023.findings-emnlp)
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| Challenge: | Existing multilingual understanding models are not capable of generating high-quality text compared with decoder-based causal language models. |
| Approach: | They propose a method to adapt a multilingual encoder to a language generator with a small number of additional parameters. |
| Outcome: | The proposed approach outperforms initialization-based methods with 9.4 BLEU on machine translation, 8.1 Rouge-L on question generation, and 5.5 METEOR on story generation. |
Can We Edit Factual Knowledge by In-Context Learning? (2023.emnlp-main)
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| Challenge: | In-context knowledge editing (IKE) is a new paradigm for NLP research that can be applied to large language models with tens or hundreds of parameters. |
| Approach: | They propose to use in-context knowledge editing (IKE) without gradient updating to edit factual knowledge without a gradient update. |
| Outcome: | The proposed method achieves a competitive success rate compared to gradient-based methods on GPT-J but with fewer side effects. |
ImageNetVC: Zero- and Few-Shot Visual Commonsense Evaluation on 1000 ImageNet Categories (2023.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) are becoming general-purpose APIs, requiring visual knowledge to be understood. |
| Approach: | They propose to evaluate the visual capability of large-scale large-language models through visual commonsense evaluation using a human-annotated dataset. |
| Outcome: | The proposed dataset compares the visual commonsense knowledge of large-scale models with those of unimodal LLMs and visually augmented models. |
MTG: A Benchmark Suite for Multilingual Text Generation (2022.findings-naacl)
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| Challenge: | Using MTG, we train and evaluate multilingual text generation models using human-annotated data. |
| Approach: | They propose a multilingual multiway text generation dataset with 400k human-annotated data that includes four generation tasks across five languages. |
| Outcome: | The proposed dataset includes four generation tasks across five languages (English, German, French, Spanish and Chinese) it provides comprehensive evaluations with diverse generation scenarios. |
Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning (P18-1)
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| Challenge: | Training a task-completion dialogue agent via reinforcement learning (RL) is costly because it requires many interactions with real users. |
| Approach: | They propose a framework that integrates planning for task-completion dialogue policy learning into a dialogue agent using a world model to mimic real user response and generate simulated experience. |
| Outcome: | The proposed framework integrates planning for task-completion dialogue policy learning with real user interaction and simulated user behavior. |
An Entropy-based Text Watermarking Detection Method (2024.acl-long)
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| Challenge: | Existing text watermarking algorithms for large language models (LLMs) are effective in identifying machine-generated texts, but they are not effective in low-entropy scenarios. |
| Approach: | They propose an Entropy-based text watermarking detection method that takes into account the influence of token entropy to better reflect the degree of watermark detection. |
| Outcome: | The proposed method is training-free and fully automated. |
OSCBench: Benchmarking Object State Change in Text-to-Video Generation (2026.acl-long)
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Xianjing Han, Bin Zhu, Shiqi Hu, Franklin Mingzhe Li, Patrick Carrington, Roger Zimmermann, Jingjing Chen
| Challenge: | Existing benchmarks focus on perceptual quality, text–video alignment, or physical plausibility, leaving a critical aspect of action understanding unexplored. |
| Approach: | They introduce a benchmark specifically designed to assess OSC performance in T2V models. |
| Outcome: | The proposed benchmark assesses the performance of open-source and proprietary T2V models on object state change (OSC) in the context of novel and compositional scenarios. |
Lego-MT: Learning Detachable Models for Massively Multilingual Machine Translation (2023.findings-acl)
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| Challenge: | Existing monolithic models for multilingual neural machine translation encounter parameter interference and inefficient inference for large models. |
| Approach: | They propose a detachable multi-way model that assigns each language to an individual branch . they use data from OPUS to build a translation benchmark covering 433 languages . |
| Outcome: | The proposed model outperforms existing models in OPUS and is faster than existing models. |
Long Chain-of-Thought Fine-tuning via Understanding-to-Reasoning Transition (2025.emnlp-main)
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Chenxin An, Zhihui Xie, Xiaonan Li, Ming Zhong, Shansan Gong, Lei Li, Jun Zhang, Jingjing Xu, Lingpeng Kong
| Challenge: | Existing research on long-context scaling in language models has focused on managing lengthy input prompts instead of producing long outputs. |
| Approach: | They propose a sequence-level curriculum learning framework that shifts a model’s focus from interpreting long chain-of-thoughts to generating them. |
| Outcome: | Experiments on rigorous reasoning benchmarks, including AIME24 and GPQA Diamond, show that the proposed approach surpasses standard fine-tuning by over 10% while maintaining robust performance on understanding tasks. |
Discriminative Deep Dyna-Q: Robust Planning for Dialogue Policy Learning (D18-1)
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| Challenge: | Existing approaches to improve the effectiveness and robustness of Deep Dyna-Q (DDQ) are based on a discriminator to control the quality of simulated experiences and to improve learning. |
| Approach: | They propose to use an RNN-based discriminator to control the quality of simulated experience to improve the effectiveness and robustness of Deep Dyna-Q. |
| Outcome: | The proposed framework outperforms DDQ by controlling the quality of simulated experience used for planning. |
Discern: Discourse-Aware Entailment Reasoning Network for Conversational Machine Reading (2020.emnlp-main)
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Yifan Gao, Chien-Sheng Wu, Jingjing Li, Shafiq Joty, Steven C.H. Hoi, Caiming Xiong, Irwin King, Michael Lyu
| Challenge: | Document interpretation and dialog understanding are the two major challenges for conversational machine reading. |
| Approach: | They propose a discourse-aware entailment reasoning network to strengthen the connection and enhance the understanding of document and dialog. |
| Outcome: | The proposed model improves document interpretation and dialog understanding on the ShARC benchmark. |
FuseGen: PLM Fusion for Data-generation based Zero-shot Learning (2024.emnlp-main)
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| Challenge: | Existing approaches to train Small Task-specific Models (STMs) using synthetic datasets are limited by the low quality of such datasets. |
| Approach: | They propose a data-generation based zero-shot learning framework that uses multiple PLMs to train small task-specific models. |
| Outcome: | The proposed framework outperforms existing methods in boosting performance across tasks. |
Aspect Sentiment Classification with Document-level Sentiment Preference Modeling (2020.acl-main)
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| Challenge: | Existing studies consider Aspect Sentiment Classification (ASC) as an independent sentence-level classification problem aspect by aspect. |
| Approach: | They propose a Cooperative Graph Attention Networks approach for cooperatively learning aspect-related sentence representation. |
| Outcome: | The proposed approach outperforms the state-of-the-art methods in document-level sentiment classification. |
SeRTS: Self-Rewarding Tree Search for Biomedical Retrieval-Augmented Generation (2024.findings-emnlp)
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Minda Hu, Licheng Zong, Hongru Wang, Jingyan Zhou, Jingjing Li, Yichen Gao, Kam-Fai Wong, Yu Li, Irwin King
| Challenge: | Existing retrieval-augmented approaches to large language models face performance limitations due to the lack of publicly available training data. |
| Approach: | They propose a plug-and-play LLM-based retrieval method called Self-Rewarding Tree Search based on Monte Carlo Tree Search and a self-rewarding paradigm to address these limitations. |
| Outcome: | The proposed method improves the performance of the BM25 retriever and surpasses the baseline of self-reflection in both efficiency and scalability. |
SAM3-I: Segment Anything with Instructions (2026.acl-long)
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Jingjing Li, Yue Feng, Yuchen Guo, Jincai Huang, Wei Ji, Qi Bi, Yongri Piao, Miao Zhang, Xiaoqi Zhao, Qiang Chen, Shihao Zou, Huchuan Lu, Li Cheng
| Challenge: | Existing methods for concept-level grounding and instruction-level reasoning use coarse representations and iterative mask filtering. |
| Approach: | They propose an instruction-following extension of the Segment Anything Model 3 family that unifies concept-level grounding and instruction-level reasoning within a single segmentation framework. |
| Outcome: | Experiments show that SAM3-I achieves appealing performance across referring and reasoning-based segmentation while maintaining its strong concept recall ability. |
Entropy-Based Decoding for Retrieval-Augmented Large Language Models (2025.naacl-long)
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| Challenge: | Despite their success, retrieval-augmented LLMs still face the distractibility issue, where the generated responses are negatively influenced by noise from both external and intrinsic knowledge sources. |
| Approach: | They propose a entropy-based document-parallel ensemble decoding method that prioritizes low-entropies from retrieved documents and incorporates a contrastive decoding mechanism that contrasts the obtained low- and high-entropic ensemble distributions with the high-end internal knowledge across layers. |
| Outcome: | The proposed method improves on open-domain question answering datasets and shows that it is highly efficient. |
Calibrating Factual Knowledge in Pretrained Language Models (2022.findings-emnlp)
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| Challenge: | Existing studies show that Pretrained Language Models can store factual knowledge, but facts stored in PLMs are not always correct. |
| Approach: | They propose a lightweight method to calibrate factual knowledge in PLMs without re-training from scratch. |
| Outcome: | The proposed method can be used to calibrate factual knowledge in PLMs without re-training from scratch. |
Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis (2024.findings-naacl)
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Wenhao Zhu, Hongyi Liu, Qingxiu Dong, Jingjing Xu, Shujian Huang, Lingpeng Kong, Jiajun Chen, Lei Li
| Challenge: | Existing studies show that large language models (LLMs) can handle multilingual machine translation (MMT) However, the multilingual translation ability of LLMs remains under-explored. |
| Approach: | They evaluate eight popular LLMs including ChatGPT and GPT-4 to determine their performance in multilingual machine translation. |
| Outcome: | The proposed model can generate moderate translation even on zero-resource languages and cross-lingual exemplars can provide better task guidance for low-resourced translation than exemplar in the same language pairs. |
A Survey on In-context Learning (2024.emnlp-main)
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Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Jingyuan Ma, Rui Li, Heming Xia, Jingjing Xu, Zhiyong Wu, Baobao Chang, Xu Sun, Lei Li, Zhifang Sui
| Challenge: | In-context learning (ICL) is a new paradigm for natural language processing . large language models (LLMs) demonstrate the ability to learn from a few examples . |
| Approach: | They propose to explore ICL to evaluate and extrapolate the ability of large language models. |
| Outcome: | The proposed methods can be used to evaluate and extrapolate the ability of large language models. |
Contextual Representation Learning beyond Masked Language Modeling (2022.acl-long)
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| Challenge: | masked language models adopt sampled embeddings as anchors to estimate and inject contextual semantics to representations. |
| Approach: | They propose a representation learning approach that uses embeddings as anchors to model contextual representations. |
| Outcome: | The proposed model achieves 5x speedup and 1.2 points average improvement over MLM. |
Multi-step Reasoning via Recurrent Dual Attention for Visual Dialog (P19-1)
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| Challenge: | Existing models for visual dialog infer the answer through multiple reasoning steps. |
| Approach: | They propose a model for visual dialog that uses multi-step reasoning to answer questions about an image. |
| Outcome: | The proposed model achieves a new state-of-the-art of 64.47% on the VisDial v1.0 dataset . |
CLongEval: A Chinese Benchmark for Evaluating Long-Context Large Language Models (2024.findings-emnlp)
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| Challenge: | Developing long-context LLMs with robust long-text capabilities is underdeveloped due to a lack of benchmarks. |
| Approach: | They propose a Chinese benchmark for evaluating long-context LLMs with Chinese capabilities. |
| Outcome: | The proposed model is based on 6 open-source LLMs and 2 commercial ones. |
Aspect Sentiment Classification Towards Question-Answering with Reinforced Bidirectional Attention Network (P19-1)
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| Challenge: | Existing studies on aspect sentiment classification focus on non-interactive reviews . a new task aims to predict sentiment polarities for specific aspects from interactive reviews based on annotated corpus . |
| Approach: | They propose a task to predict aspects from interactive QA style reviews using an annotated corpus. |
| Outcome: | The proposed approach is compared with state-of-the-art methods against a high-quality corpus of data. |
Vocabulary Learning via Optimal Transport for Neural Machine Translation (2021.acl-long)
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| Challenge: | Empirical results show that VOLT beats widely-used vocabularies in diverse scenarios, including WMT-14 English-German translation, TED bilingual translation, and TED multilingual translation. |
| Approach: | They propose a token dictionary solution that can be used without trial training to find the best dictionary with a proper size. |
| Outcome: | The proposed solution beats widely-used vocabularies in English-German translation, TED bilingual translation, and TED multilingual translation. |
Sentiment Classification towards Question-Answering with Hierarchical Matching Network (D18-1)
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Chenlin Shen, Changlong Sun, Jingjing Wang, Yangyang Kang, Shoushan Li, Xiaozhong Liu, Luo Si, Min Zhang, Guodong Zhou
| Challenge: | Existing methods to classify QA text contain rich sentiment information. |
| Approach: | They propose a task/method to address QA sentiment analysis by annotating QA text pair with annotation guidelines. |
| Outcome: | The proposed method can learn the matching vectors of each Q-sentence, A-sentent unit. |
LegoMT2: Selective Asynchronous Sharded Data Parallel Training for Massive Neural Machine Translation (2025.findings-acl)
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| Challenge: | Existing methods to train a single model for massive languages have huge communication overheads and parameter interference. |
| Approach: | They propose an efficient training approach with an asymmetric multi-way model architecture for massive multilingual neural machine translation. |
| Outcome: | The proposed model is 16.2 faster than the distributed training method for M2M-100-12B while improving the translation performance by an average of 2.2 BLEU on Flores-101. |
Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement Learning Approach (P18-1)
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| Challenge: | Existing studies for sentiment-to-sentiment "translation" only change the underlying sentiment and fail to keep the semantic content. |
| Approach: | They propose a cycled reinforcement learning method that combines neutralization module and emotionalization module. |
| Outcome: | The proposed method outperforms state-of-the-art systems on Yelp and Amazon review datasets. |
Unifying Cross-Lingual and Cross-Modal Modeling Towards Weakly Supervised Multilingual Vision-Language Pre-training (2023.acl-long)
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| Challenge: | Existing studies address the problem of translating English data into other languages, but they are limited in form and scale. |
| Approach: | They propose a framework to unify cross-lingual and cross-modal pre-training by using English data. |
| Outcome: | The proposed framework unifies cross-lingual and cross-modal pre-training on different data. |
EviReport: From Reasoned Outlines to Evidence Tracked Long-Form Reports (2026.findings-acl)
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Zihan Liu, Jianhui li, Zexin Wang, Fei Sun, Jingjing LI, Zheyuan Li, Ke Xiang, Hang Cui, Houhua Gong, Changhua Pei, Gaogang Xie
| Challenge: | Evidence-intensive reports often produce fluent but under-supported drafts . eviReport is an evidence-grounded workflow for automated long-form report generation . |
| Approach: | They propose an evidence-tracked workflow that organizes corpus evidence into compact, traceable units and retrieves query-relevant subgraphs into retrieval-ready packages. |
| Outcome: | The proposed workflow outperforms baselines in factual coverage, factual accuracy and visual evidence integration. |
Contextual Text Style Transfer (2020.findings-emnlp)
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| Challenge: | Existing methods for text style transfer are limited by the lack of parallel data. |
| Approach: | They propose a task to translate a sentence into a desired style with its surrounding context taken into account. |
| Outcome: | The proposed model outperforms state-of-the-art methods across style accuracy, content preservation and contextual consistency metrics. |
CodeDPO: Aligning Code Models with Self Generated and Verified Source Code (2025.acl-long)
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| Challenge: | Existing training methods for code generation do not improve code correctness and efficiency. |
| Approach: | They propose a framework that integrates preference learning into code generation to improve code correctness and efficiency. |
| Outcome: | The proposed framework improves code correctness and efficiency by integrating preference learning into code generation. |
Human-Like Decision Making: Document-level Aspect Sentiment Classification via Hierarchical Reinforcement Learning (D19-1)
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Jingjing Wang, Changlong Sun, Shoushan Li, Jiancheng Wang, Luo Si, Min Zhang, Xiaozhong Liu, Guodong Zhou
| Challenge: | Recent neural networks have shown promising results on Document-level Aspect Sentiment Classification (DASC) however, these approaches often offer little transparency w.r.t. their inner working mechanisms and lack interpretability. |
| Approach: | They propose a Hierarchical Reinforcement Learning approach to DASC that incorporates clause selection and word selection strategies to tackle the data noise problem. |
| Outcome: | The proposed approach over the state-of-the-art approaches shows impressive performance over the current baselines. |
Can Language Models Understand Physical Concepts? (2023.emnlp-main)
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| Challenge: | Existing language models do not understand basic physical concepts in the human world. |
| Approach: | They propose a method to transfer embodied knowledge from visual models to LMs . they use visual concepts and embodies concepts learned from interaction with the world . |
| Outcome: | The proposed method achieves comparable performance with scaling up parameters of LMs 134. |
HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training (2020.emnlp-main)
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| Challenge: | HERO is a framework for large-scale video+language omni-representation learning. |
| Approach: | They propose a framework for large-scale video+language omni-representation learning that encodes multimodal inputs in a hierarchical structure and uses Masked Language Modeling and Masked Frame Modeling to train models. |
| Outcome: | The proposed framework achieves state-of-the-art on multiple benchmarks over text-based video/video-moment retrieval, video question answering (QA), Video-and-language Inference and video Captioning tasks across different domains. |
Improving Question Generation With to the Point Context (D19-1)
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| Challenge: | Existing sequence-to-sequence neural models may not be able to identify answer-relevant context words for question generation. |
| Approach: | They propose to model the unstructured sentence and the structured answer-relevant relation for question generation by combining to the point context and unstructure. |
| Outcome: | Experiments show that the proposed model improves on the unstructured sentence and the structured answer-relevant relation. |