Papers by Daling Wang

STICKERCONV: Generating Multimodal Empathetic Responses from Scratch (2024.acl-long)

Challenge:	Existing research on multimodal dialogues focuses on textual response generation and visual response selection based on the dialogue context.
Approach:	They propose a generative model framework for multimodal dialogue response generation that ground the conversation on an image.
Outcome:	The proposed system provides users with an enhanced conversational experience.

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Yiqun Zhang, Fanheng Kong, Peidong Wang, Shuang Sun, SWangLing SWangLing, Shi Feng, Daling Wang, Yifei Zhang, Kaisong Song

Resource-Limited Joint Multimodal Sentiment Reasoning and Classification via Chain-of-Thought Enhancement and Distillation (2026.findings-acl)

Challenge:	Prior studies on stickers focused on sentiment analysis and recommendation systems, overlooking their vast potential in empathetic response generation.
Approach:	They propose a multimodal empathetic dialogue dataset, STICKERCONV, which simulates human behavior with stickers, and propose evaluative metrics based on LLM.
Outcome:	The proposed framework generates contextually relevant and emotionally resonant multimodal empathetic responses, contributing to the advancement of more nuanced and engaging e-dialog systems.

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Haonan Shangguan, Xiaocui Yang, Shi Feng, Daling Wang, Yifei Zhang, Feiliang Ren, Ge Yu

Information Retrieval

Challenge:	Current approaches for Multimodal Sentiment Analysis (MSA) rely on parameter-heavy LLMs for classification, overlooking multimodal sentiment reasoning generation in resource-limited environments.
Approach:	They propose a multimodal sentiment reasoning distillation model that employs a teacher-assistant-student paradigm to address deployment constraints in resource-limited environments.
Outcome:	The proposed model performs well on a resource-limited JMSRC task with only 3B parameters and shows generalization and interpretability.

Personalized Microblog Sentiment Classification via Adversarial Cross-lingual Multi-task Learning (D18-1)

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Weichao Wang, Shi Feng, Wei Gao, Daling Wang, Yifei Zhang

MulZDG: Multilingual Code-Switching Framework for Zero-shot Dialogue Generation (2022.coling-1)

Challenge:	Existing personalized microblog sentiment classification methods suffer from the insufficiency of discriminative tweets for personalization learning.
Approach:	They propose to use user-attention-based Convolutional Neural Networks to capture individuality and opinion bias in microblog posts and a novel adversarial cross-lingual learning framework to enrich the user post representation.
Outcome:	The proposed method outperforms state-of-the-art baseline algorithms with large margins on English and Chinese microblog datasets.

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Yongkang Liu, Shi Feng, Daling Wang, Yifei Zhang

Alleviating Sparsity of Open Knowledge Graphs with Ternary Contrastive Learning (2022.findings-emnlp)

Challenge:	Existing zero-shot dialogue generation systems rely on large-scale pre-trained language models.
Approach:	They propose a multilingual learning framework for zero-shot dialogue generation that can transfer knowledge from an English corpus to a non-English corpus with zero samples.
Outcome:	The proposed framework can transfer knowledge from an English corpus to a non-English corpus with zero samples.

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Qian Li, Shafiq Joty, Daling Wang, Shi Feng, Yifei Zhang

Contrastive Learning with Generated Representations for Inductive Knowledge Graph Embedding (2023.findings-acl)

Challenge:	Existing approaches to learning KG triplets ignore ternary propagation patterns and ignore zero-shot, few-shot and synonymity problems.
Approach:	They propose a framework for contrastive learning based on ternary propagation patterns among head, relation and tail.
Outcome:	Experiments on benchmarks show that TernaryCL is superior to state-of-the-art models.

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Qian Li, Shafiq Joty, Daling Wang, Shi Feng, Yifei Zhang, Chengwei Qin

AnnaAgent: Dynamic Evolution Agent System with Multi-Session Memory for Realistic Seeker Simulation (2025.findings-acl)

Challenge:	Existing methods for inductive knowledge Graphs are limited by sparsity and implicit transfer.
Approach:	They propose a Contrastive Learning framework with graph guided Variational autoencoder on Meta-KGs to capture and transfer entities.
Outcome:	The proposed framework outperforms state-of-the-art methods with extensive experiments.

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Ming Wang, Peidong Wang, Lin Wu, Xiaocui Yang, Daling Wang, Shi Feng, Yuxin Chen, Bixuan Wang, Yifei Zhang

DeepMed: Building a Medical DeepResearch Agent via Multi-hop Med-Search Data and Turn-Controlled Agentic Training & Inference (2026.findings-acl)

Challenge:	Existing models of seeker simulations are limited by the cost and ethical concerns of involving real seekers in mental health research.
Approach:	They propose an emotional and cognitive dynamic agent system equipped with tertiary memory to enable dynamic control of the simulator's configurations.
Outcome:	The proposed system achieves more realistic seeker simulation compared to baselines.

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Zihan Wang, Hao Wang, Shi Feng, Xiaocui Yang, Daling Wang, Yiqun Zhang, Jinghao Lin, Xiaozhong Ji, Haihua Yang

Reasoning

Challenge:	Medical reasoning models are constrained by parametric knowledge and can induce hallucinations and spurious attributions.
Approach:	They propose a model that uses a multi-hop med-search QA synthesis method to apply the DR paradigm in medical contexts.
Outcome:	The proposed model outperforms larger medical reasoning models on medical benchmarks.

EmpCRL: Controllable Empathetic Response Generation via In-Context Commonsense Reasoning and Reinforcement Learning (2024.lrec-main)

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Mingxiu Cai, Daling Wang, Shi Feng, Yifei Zhang

Why Do More Experts Fail? A Theoretical Analysis of Model Merging (2026.acl-long)

Challenge:	Existing studies lack the perception of fine-grained dialogue emotion propagation, and have limitations in reasoning about the intentions of users on cognition, which affect the quality of empathetic response.
Approach:	They propose to use commonsense reasoning and reinforcement learning to generate empathetic response based on in-context commonsensing and contextual reasoning to broaden cognitive boundaries.
Outcome:	The proposed model outperforms state-of-the-art models in automatic and human evaluation.

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Zijing Wang, Xingle Xu, YongKang Liu, Yiqun Zhang, Peiqin Lin, Shi Feng, Daling Wang, Xiaocui Yang, Hinrich Schuetze

Challenge:	Existing methods for model merging struggle to maintain performance gains as the number of merged models increases.
Approach:	They propose a Reparameterized Heavy-Tailed method to extend the merged model’s coverage and enhance performance.
Outcome:	The proposed method extends the merged model’s coverage and enhances performance on 19 benchmarks, including knowledge-intensive and general-purpose tasks.

GenPT: Beyond Self-Report for Reliable LLM Psychometrics via Generative Projective Testing (2026.acl-long)

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Ming Wang, Shuang Wu, Bixuan Wang, Lu Lin, Yuxin Chen, Xiaocui Yang, Daling Wang, Shi Feng, Yifei Zhang, Yufan Sun

Challenge:	Large language models (LLMs) inherit contamination from training corpora, directional bias under social-desirability framing, and limited responsiveness to context beyond the item text.
Approach:	They propose a paradigm that reformulates TAT, Rorschach, and SCT with newly generated stimuli and organises assessment as a three-stage pipeline.
Outcome:	The proposed paradigm reformulates TAT, Rorschach, and SCT with newly generated stimuli and organises assessment as a three-stage pipeline.

TOOL-ED: Enhancing Empathetic Response Generation with the Tool Calling Capability of LLM (2025.coling-main)

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Huiying Cao, Yiqun Zhang, Shi Feng, Xiaocui Yang, Daling Wang, Yifei Zhang

Text Generation

Challenge:	Empathetic conversation is a crucial characteristic in daily conversations between individuals.
Approach:	They propose an Emotional Knowledge Tool Calling framework which encapsulates commonsense knowledge bases as empathetic tools, enabling LLMs to integrate external knowledge flexibly.
Outcome:	The proposed framework can generate empathetic responses effectively on the TOOL-ED dataset.

MoLAN: A Unified Modality-Aware Noise Dynamic Editing Framework for Multimodal Sentiment Analysis (2026.findings-acl)

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Xingle Xu, YongKang Liu, Dexian Cai, Shi Feng, Xiaocui Yang, Daling Wang, Yifei Zhang

HiFT: A Hierarchical Full Parameter Fine-Tuning Strategy (2024.emnlp-main)

Challenge:	Existing approaches to multimodal sentiment analysis treat entire modality as an independent unit for feature enhancement or denoising, which often suppresses redundant noise at the cost of weakening critical information.
Approach:	They propose a ModaLity-aware noise dynAmic editiNg framework that performs modality-awful block partitioning by dividing features of each modality into multiple blocks.
Outcome:	Experiments on five models and four datasets show that MoLAN+ achieves the state-of-the-art performance.

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YongKang Liu, Yiqun Zhang, Qian Li, Tong Liu, Shi Feng, Daling Wang, Yifei Zhang, Hinrich Schuetze

SemanticCamo: Jailbreaking Large Language Models through Semantic Camouflage (2025.findings-acl)

Challenge:	Existing approaches to fine-tuning language models use zeroth-order optimizers to conserve GPU memory.
Approach:	They propose a full-parameter fine-tuning strategy which updates a subset of parameters at each training step.
Outcome:	The proposed approach reduces the amount of gradients and optimizer state parameters residing in GPU memory at the same time, thereby reducing GPU memory usage.

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Jihui Yan, Xiaocui Yang, Daling Wang, Shi Feng, Yifei Zhang, Yinzhi Zhao

MUSE: A Multimodal Conversational Recommendation Dataset with Scenario-Grounded User Profiles (2025.findings-acl)

Challenge:	Large Language Models (LLMs) have made safety issues of LLMs more prominent and critical.
Approach:	They propose a framework which attacks LLMs through semantic camouflage and replaces unsafe content with semantic features to conceal malicious intent .
Outcome:	The proposed framework outperforms existing models in over 80% of cases and is highly effective against various defenses.

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Zihan Wang, Xiaocui Yang, YongKang Liu, Shi Feng, Daling Wang, Yifei Zhang

SAD: A Large-Scale Strategic Argumentative Dialogue Dataset (2026.acl-long)

Challenge:	Existing research focuses solely on text, leaving a gap with practical applications.
Approach:	They propose to synthesize a multimodal conversational recommendation dataset using multimodal large language models to automatically synthesized data from 7,000 conversations in the Clothing domain.
Outcome:	The proposed dataset contains 83,148 utterances from 7,000 conversations centered around the Clothing domain.

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YongKang Liu, Jiayang Yu, Mingyang Wang, Yiqun Zhang, Ercong Nie, Shi Feng, Daling Wang, Kaisong Song, Hinrich Schuetze

A Co-Attention Neural Network Model for Emotion Cause Analysis with Emotional Context Awareness (D18-1)

Challenge:	Argumentation is a key part of human reasoning and decision-making . existing argumentative corpora focus on single-turn settings, but multi-turn dialogues are often realized as multi-turned dialogues .
Approach:	They present a dataset for strategic multi-turn argumentation dialogues . they annotate each utterance with five strategy types, allowing multiple strategies per utterrance .
Outcome:	The proposed dataset shows that explicit prompting improves fluency, stylistic coherence and persuasiveness.

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Xiangju Li, Kaisong Song, Shi Feng, Daling Wang, Yifei Zhang

Sentiment Analysis

Challenge:	Existing methods ignore the contexts around the emotion word which can provide an emotion cause clue.
Approach:	They propose a co-attention neural network model for emotion cause analysis with emotional context awareness.
Outcome:	The proposed model outperforms the state-of-the-art methods.

Boundary Detection with BERT for Span-level Emotion Cause Analysis (2021.findings-acl)

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Xiangju Li, Wei Gao, Shi Feng, Yifei Zhang, Daling Wang

Pixel-Level Reasoning Segmentation via Multi-turn Conversations (2025.acl-long)

Challenge:	Emotion cause analysis (ECA) is an emerging topic in natural language processing, which aims to identify the reasons behind a given emotion.
Approach:	They propose to detect the precise boundaries of text spans conveying accurate emotion causes from the given context by a sequence labeling and position identification problem.
Outcome:	The proposed methods outperform existing models on two benchmark datasets on the emotion cause analysis task.

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Dexian Cai, Xiaocui Yang, YongKang Liu, Daling Wang, Shi Feng, Yifei Zhang, Soujanya Poria

DialogConv: A Lightweight Fully Convolutional Network for Multi-view Response Selection (2022.emnlp-main)

Challenge:	Existing visual perception systems focus on region-level segmentation in single-turn dialogues . existing systems cannot reason at the pixel level and comprehend dynamic user intent .
Approach:	They propose a task that tracks evolving user intent via multi-turn interactions for fine-grained segmentation.
Outcome:	The proposed method outperforms existing baselines in segmentation and reasoning metrics.

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Yongkang Liu, Shi Feng, Wei Gao, Daling Wang, Yifei Zhang

DR-HM: Distill-then-Reinforce Training with Cognition-Aware Data Synthesis for Harmful Meme Detection (2026.findings-acl)

Challenge:	Existing retrieval-based dialogue systems suffer from slow inference or huge number of parameters.
Approach:	They propose a lightweight fully convolutional architecture for response selection using convolution.
Outcome:	The proposed architecture extracts matching features of context and response from 3D views.

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Zihan Cheng, Jianxiang Ma, Xiaocui Yang, Peidong Wang, Wen Zhang, Shi Feng, Daling Wang, Yifei Zhang, Mingfu Zhang

TUNA: Comprehensive Fine-grained Temporal Understanding Evaluation on Dense Dynamic Videos (2025.acl-long)

Challenge:	Current methods for harmful meme detection lack the knowledge required to identify such hate . current methods lack the ability to identify cultural stereotypes and visual metaphors .
Approach:	They propose a framework that decomposes meme analysis into a human-inspired reasoning process . they propose DR-HM to transfer knowledge from closed-source models while mitigating biases .
Outcome:	The proposed framework outperforms existing methods on three benchmark datasets.

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Fanheng Kong, Jingyuan Zhang, Hongzhi Zhang, Shi Feng, Daling Wang, Linhao Yu, Xingguang Ji, Yu Tian, V. W., Fuzheng Zhang

Visual Data in NLP

Challenge:	Existing benchmarks for video understanding often focus on specific aspects, overlooking the holistic nature of video content.
Approach:	They propose a temporal-oriented benchmark for fine-grained understanding on dense dynamic videos with two complementary tasks: captioning and QA.
Outcome:	The proposed model performs well on diverse video scenarios and dynamic videos, with interpretable and robust evaluation criteria.

Learning to Improve Persona Consistency in Multi-party Dialogue Generation via Text Knowledge Enhancement (2022.coling-1)

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Dongshi Ju, Shi Feng, Pengcheng Lv, Daling Wang, Yifei Zhang

KC-ISA: An Implicit Sentiment Analysis Model Combining Knowledge Enhancement and Context Features (2022.coling-1)

Challenge:	Existing methods suffer from incomprehensive persona tags that have unique and obscure meanings to describe human’s personality.
Approach:	They propose a graph convolution network model with addressee selecting mechanism that integrates personas, dialogue utterances, and external text knowledge in a unified graph.
Outcome:	The proposed model outperforms baselines by large margins and improves persona consistency in the generated responses.

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Minghao Xu, Daling Wang, Shi Feng, Zhenfei Yang, Yifei Zhang

Sentiment Analysis

Challenge:	Existing research results on explicit sentiment analysis are limited . implicit sentiment analysis is a process of analyzing text based on whether it contains explicit sentiment words.
Approach:	They propose a model that integrates external knowledge and contextual features . they use a knowledge graph to supplement implicit sentiment expression .
Outcome:	The proposed model can achieve better results on the SMP2019 implicit sentiment analysis dataset.

JX4MEI: Multimodal Semantically-Enhanced LLM for Joint Multimodal Emotion-Intent Explanation and Classification (2026.findings-acl)

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YiJie Huang, Xiaocui Yang, Shi Feng, Daling Wang, Yifei Zhang, Ning Yuan, Zhuoyue Jia, Wen Zhang

Information Retrieval

Challenge:	Existing multimodal emotion and intent recognition tasks focus on classification, not rationale and intrinsic connections between these states.
Approach:	They propose a task that requires models to jointly predict emotion and intent while generating natural language explanations for why they co-occur.
Outcome:	The proposed model outperforms baseline models in prediction and explanation generation.

Multimodal Sentiment Detection Based on Multi-channel Graph Neural Networks (2021.acl-long)

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Xiaocui Yang, Shi Feng, Yifei Zhang, Daling Wang

Improving Role-Oriented Dialogue Summarization with Interaction-Aware Contrastive Learning (2024.lrec-main)

Challenge:	Existing studies only considered the representation of a single image-text post . Fig. 1 shows that multimodal sentiment expressions have global characteristics .
Approach:	They propose a multi-channel Graph Neural Networks with Sentiment-awareness approach for image-text sentiment detection.
Outcome:	The proposed approach is effective for image-text sentiment detection on three publicly available datasets.

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Weihong Guan, Shi Feng, Daling Wang, Faliang Huang, Yifei Zhang, Yuan Cui

Look Within or Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning (2026.acl-long)

Challenge:	Existing methods for encoding dialogues do not capture interaction information between roles, thus ignore interaction-related key information.
Approach:	They propose a contrastive learning based interaction-aware model for the role-oriented dialogue summarization namely CIAM and use it to train the decoder to learn role-level interaction.
Outcome:	The proposed model captures interaction information between different roles and produces informative summaries on two public datasets.

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YongKang Liu, Xingle Xu, Ercong Nie, Zijing Wang, Shi Feng, Daling Wang, Qian Li, Hinrich Schuetze

PlaM: Training-Free Plateau-Guided Model Merging for Better Visual Grounding in MLLMs (2026.findings-acl)

Challenge:	Parameter-Efficient Fine-Tuning (PEFT) is an alternative to Full-Parameter Fine-tuning, but its effectiveness on complex tasks such as reasoning and instruction-following remains unclear.
Approach:	They propose to use PEFT to reduce the number of trainable parameters while freezing the weights of LLMs.
Outcome:	The proposed methods perform well on standard tasks, but weaknesses on complex and adversarial settings call for new directions beyond current paradigms.

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Zijing Wang, YongKang Liu, Mingyang Wang, Ercong Nie, Deyuan Chen, Zhengjie Zhao, Shi Feng, Daling Wang, Xiaocui Yang, Yifei Zhang, Hinrich Schuetze

Answer-guided and Semantic Coherent Question Generation in Open-domain Conversation (D19-1)

Challenge:	Multimodal instruction fine-tuning degrades textual reasoning capability, undermining multimodal performance.
Approach:	They propose a plateau-guided model merging method that selectively injects base language model parameters into MLLMs to mitigate this degradation.
Outcome:	The proposed framework reduces multimodal instruction fine-tuning degradation by incorporating a plateau-guided model merging method into MLLMs.

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Weichao Wang, Shi Feng, Daling Wang, Yifei Zhang

CIRAG: Construction–Integration Retrieval and Adaptive Generation for Multi-hop Question Answering (2026.acl-long)

Challenge:	Existing methods for question generation suffer from dullness and deviation problem, which can lead to deviated or dull questions.
Approach:	They propose two methods to enhance semantic coherence between question and answer by using a coherent score and adversarial training to explicitly control question generation.
Outcome:	The proposed methods outperform state-of-the-art baseline algorithms with large margins in raising semantic coherent questions.

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Zili Wei, Yilin Wang, Xiaocui Yang, Shi Feng, Weidong Bao, Daling Wang, Zihan Wang, Yifei Zhang

Information Retrieval

Challenge:	Existing methods for iterative retrieval-augmented generation (iRAG) suffer from greedy single-path expansion and granularity–demand mismatch .
Approach:	They propose a model that constructs candidate triples and history-conditionally integrates them to distill core triples to generate the next-hop query.
Outcome:	The proposed model mitigates the greedy single-path expansion and granularity–demand mismatch by preserving multiple plausible evidence chains.

Language Models as Continuous Self-Evolving Data Engineers (2025.emnlp-main)

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Peidong Wang, Ming Wang, Zhiming Ma, Xiaocui Yang, Shi Feng, Daling Wang, Yifei Zhang, Kaisong Song

NEAT: Neuron-Based Early Exit for Large Reasoning Models (2026.findings-acl)

Challenge:	Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their further evolution is often hampered by the scarcity of high-quality training data and the heavy reliance of traditional methods on expert-labeled data.
Approach:	They propose a paradigm that enables LLMs to train themselves by generating, cleaning, reviewing and annotating data with preference information.
Outcome:	The proposed model can generate, clean, review, and annotate data with preference information significantly reducing time and cost of post-training data construction.

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Kang Liu, YongKang Liu, Xiaocui Yang, Peidong Wang, Wen Zhang, Shi Feng, Yifei Zhang, Daling Wang

Reasoning

Challenge:	Existing approaches to reduce overthinking require additional rollout computation or externally labeled datasets.
Approach:	They propose a Neuron-based Early reAsoning exiT framework that monitors neuron-level activation dynamics to enable training-free early exits.
Outcome:	The proposed framework reduces the amount of reasoning steps generated by LRMs while maintaining accuracy.

PVGRU: Generating Diverse and Relevant Dialogue Responses via Pseudo-Variational Mechanism (2023.acl-long)

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Yongkang Liu, Shi Feng, Daling Wang, Yifei Zhang, Hinrich Schütze

MTRouter: Cost-Aware Multi-Turn LLM Routing with History–Model Joint Embeddings (2026.acl-long)

Challenge:	Existing generative models for dialogue use the last hidden state to summarize the history of the dialogue.
Approach:	They propose a Pseudo-Variational Gated Recurrent Unit (PVGRU) that summarises the accumulated distribution variations of subsequences and builds a model based on it.
Outcome:	The proposed model can improve diversity and relevance of responses on two benchmark datasets.

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Yiqun Zhang, Hao Li, Zihan Wang, Shi Feng, Xiaocui Yang, Daling Wang, Bo Zhang, Lei Bai, Shuyue Hu

SAFE-QAQ: End-to-End Slow-Thinking Audio-Text Fraud Detection via Reinforcement Learning (2026.acl-long)

Challenge:	Multi-turn, long-horizon tasks require dozens of sequential model calls per episode.
Approach:	They propose a cost-aware multi-turn LLM routing tool which encodes interaction history and candidate models into joint history–model embeddings and learns an outcome estimator from logged trajectories to predict turn-level model utility.
Outcome:	The proposed model reduces cost and performance by 58.7% on ScienceWorld and on Humanity’s Last Exam (HLE) and even reduces costs for held-out tasks.

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Peidong Wang, Zhiming Ma, Xin Dai, YongKang Liu, Shi Feng, Xiaocui Yang, Wenxing Hu, Zhihao Wang, Mingjun Pan, Li Yuan, Daling Wang

Cat-MoD: Accelerating Multimodal Alignment via Caption Token Guided Asymmetric Mixture-of-Depths (2026.acl-long)

Challenge:	Existing methods for fraud detection rely on transcribed text, lacking acoustic cues . a proposed framework for audio-based slow-thinking fraud detection eliminates transcription errors .
Approach:	They propose a framework for audio-based slow-thinking fraud detection that eliminates transcription errors and rewards slow-thought reasoning by capturing fine-grained audio details.
Outcome:	The proposed method improves accuracy, inference efficiency, and real-time processing capabilities.

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YiJie Huang, Xiaocui Yang, Shi Feng, Wen Zhang, Kaisong Song, Yifei Zhang, Daling Wang

Answer-Supervised Question Reformulation for Enhancing Conversational Machine Comprehension (D19-58)

Challenge:	Existing query-based alignment modules enforce uniform cross-attention across all layers, leading to computational redundancy.
Approach:	They propose a framework that allows for asynchronous query-based alignment with large-scale visual features.
Outcome:	The proposed framework matches or surpasses baseline performance while reducing alignment FLOPs by approximately 37% during training and inference.

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Qian Li, Hui Su, Cheng Niu, Daling Wang, Zekang Li, Shi Feng, Yifei Zhang

RATION: Entropy-Driven Task-Adaptive Visual Attention Allocation Framework for Multimodal Reasoning (2026.findings-acl)

Challenge:	Existing question reformulation models are based on supervised question labels without considering feedback information from answers.
Approach:	They propose a question reformulation model that integrates conversational history information with reinforcement learning.
Outcome:	The proposed model is more effective in conversational machine comprehension with reinforcement learning.

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Xingle Xu, Fanheng Kong, Dexian Cai, Shi Feng, Xiaocui Yang, Daling Wang, Yifei Zhang

BERT-BC: A Unified Alignment and Interaction Model over Hierarchical BERT for Response Selection (2024.lrec-main)

Challenge:	Prior studies have focused on strengthening multimodal reasoning by improving representation alignment or increasing computation, but these methods do not characterize the differences in visual demands across tasks.
Approach:	They propose an entropy-driven task-adaptive visual attention allocation framework that uses visual attention entropic as a control signal to dynamically allocate attention according to task demands.
Outcome:	The proposed framework achieves consistent performance gains across diverse reasoning tasks, datasets, and models, providing a clear direction toward more reliable multimodal reasoning.

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Zhenfei Yang, Beiming Yu, Yuan Cui, Shi Feng, Daling Wang, Yifei Zhang

Few-shot Joint Multimodal Aspect-Sentiment Analysis Based on Generative Multimodal Prompt (2023.findings-acl)

Challenge:	Recent performance boosting for dialogue response selection task achieved by Cross-Encoder based models is limited and the learned models have poor generalization capability in realistic scenarios.
Approach:	They propose a model that combines the representation-based Bi-Encoder and interaction-based Cross-Encoding to achieve better semantic representation.
Outcome:	The proposed model can achieve state-of-the-art performance on three benchmark datasets for multi-turn response selection.

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Xiaocui Yang, Shi Feng, Daling Wang, Qi Sun, Wenfang Wu, Yifei Zhang, Pengfei Hong, Soujanya Poria

Sentiment Analysis

Challenge:	Existing studies require massive labeled data to train models for multimodal data analysis.
Approach:	They propose a novel multimodal prompt model that captures specific aspect terms in a few-shot scenario.
Outcome:	The proposed model outperforms baselines on two MABSA-related tasks on a few-shot dataset.

ES4R: Speech Encoding Based on Prepositive Affective Modeling for Empathetic Response Generation (2026.acl-long)

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Zhuoyue Gao, Xiaohui Wang, Xiaocui Yang, Wen Zhang, Daling Wang, Shi Feng, Yifei Zhang