Papers with MMD
Label-Aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognition (N18-1)
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Zhenghui Wang, Yanru Qu, Liheng Chen, Jian Shen, Weinan Zhang, Shaodian Zhang, Yimei Gao, Gen Gu, Ken Chen, Yong Yu
| Challenge: | NER is a fundamental problem for medical text mining because of the difference of specialties and cost of human annotation. |
| Approach: | They propose a label-aware double transfer learning framework for medical NER from electronic medical records. |
| Outcome: | The proposed framework improves accuracy over strong baselines on 12 cross-specialty NER tasks. |
CDAˆ2: Counterfactual Diffusion Augmentation for Cross-Domain Adaptation in Low-Resource Sentiment Analysis (2025.coling-main)
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| Challenge: | Domain adaptation is widely employed in cross-domain sentiment analysis, but concerns have been raised regarding their robustness and sensitivity to data distribution shift. |
| Approach: | They propose a framework CDA2 for cross-domain adaptation in low-resource sentiment analysis which employs counterfactual diffusion augmentation. |
| Outcome: | The proposed framework generates high-quality counterfactual target samples and achieves state-of-the-art performance on benchmark datasets. |
UniTranSeR: A Unified Transformer Semantic Representation Framework for Multimodal Task-Oriented Dialog System (2022.acl-long)
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| Challenge: | Existing studies on multimodal task-oriented dialog systems follow the pipeline to learn intra-modal features separately and then conduct simple feature concatenation or attention-based feature fusion to generate responses. |
| Approach: | They propose a Unified Transformer Semantic Representation framework with feature alignment and intention reasoning for multimodal dialog systems that embed multimodal features into a unified Transformer semantic space to prompt inter-modal interactions. |
| Outcome: | The proposed framework significantly outperforms state-of-the-art approaches on the representative MMD dataset. |
Domain Adaptation for Question Answering via Question Classification (2022.coling-1)
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| Challenge: | Question answering systems often experience performance deterioration upon user-generated questions. |
| Approach: | They propose a question classification framework to help QA domains adapt to different domains. |
| Outcome: | The proposed framework improves on state-of-the-art datasets against multiple datasets. |
Interactive Evaluation for Medical LLMs via Task-oriented Dialogue System (2025.coling-main)
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| Challenge: | In typical medical scenarios, doctors often ask a set of questions to gain a comprehensive understanding of patients’ conditions. |
| Approach: | They propose to use multi-turn medical dialogue evaluation to evaluate proactive communication and diagnostic capabilities of medical Large Language Models (LLMs) . |
| Outcome: | The proposed model outperforms existing models on multi-turn question-answering datasets and is therefore cost-effective. |
Konooz: Multi-domain Multi-dialect Corpus for Named Entity Recognition (2025.findings-acl)
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| Challenge: | Using the Wojood framework, we compare existing Arabic Named Entity Recognition models with domain and dialect divergence and resource scarcity. |
| Approach: | They propose a multi-dimensional Arabic named entity corpus covering 16 dialects across 10 domains and an annotation scheme using the Wojood guidelines. |
| Outcome: | The proposed model performs better on 16 dialects across 10 domains and 16 domains, while other models struggle with different dialects and domains. |
Hope ‘The Paragraph Guy’ explains the rest : Introducing MeSum, the Meme Summarizer (2024.findings-emnlp)
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| Challenge: | a lack of large datasets for supervised learning and resource-intensive vision language models have hindered the development of meme comprehension. |
| Approach: | They propose a framework to bridge the gap between meme comprehension and vision language models by using a multimodal dataset. |
| Outcome: | The proposed framework outperforms existing methods in the meme comprehension test. |
Zero- and Few-Shot Event Detection via Prompt-Based Meta Learning (2023.acl-long)
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| Challenge: | Existing methods for event detection often fail to detect unseen or rare events due to the lack of domain knowledge. |
| Approach: | They propose a meta learning-based framework for zero-shot event detection that uses a prompt-based prompt and a trigger-aware soft verbalizer to efficiently project output to unseen tasks. |
| Outcome: | The proposed framework performs state-of-the-art in zero-shot and few-shot scenarios on benchmark datasets FewEvent and MAVEN. |
PDALN: Progressive Domain Adaptation over a Pre-trained Model for Low-Resource Cross-Domain Named Entity Recognition (2021.emnlp-main)
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| Challenge: | Existing approaches to Named Entity Recognition (NER) are limited in labeled resources and domain shift. |
| Approach: | They propose a progressive domain adaptation knowledge distillation approach to adapt high-resource domains to low-resourced target domains by employing three components to achieve superior domain adaptability. |
| Outcome: | The proposed approach can adapt high-resource domains to low-resourced target domains even if they are diverse in terms and writing styles. |
MMD-ERE: Multi-Agent Multi-Sided Debate for Event Relation Extraction (2025.coling-main)
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| Challenge: | Existing research indicates that LLMs can be overconfident and stubborn. |
| Approach: | They propose a multi-agent multi-sided debate approach for event relation extraction which explores the understanding of event relations between different participants before and after the debate. |
| Outcome: | The proposed approach outperforms established baselines on various ERE tasks and LLMs. |
Ordinal and Attribute Aware Response Generation in a Multimodal Dialogue System (P19-1)
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| Challenge: | Existing multimodal dialogue systems are based on unimodal sources, capturing information from text and image. |
| Approach: | They propose a position and attribute aware attention mechanism to learn enhanced image representation conditioned on the user utterance. |
| Outcome: | The proposed model outperforms the state-of-the-art models on text similarity metrics. |
Topic Modeling with Wasserstein Autoencoders (P19-1)
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| Challenge: | Existing probabilistic topic models are based on latent Dirichlet allocations and collapsed Gibbs sampling. |
| Approach: | They propose a novel topic model that enforces Dirichlet prior on latent document-topic vectors and a kernel kernel to minimize the Maximum Mean Discrepancy (MMD) They propose to measure the diversity of the produced topics and to use the widely used coherence measure NPMI to evaluate topic quality. |
| Outcome: | The proposed model performs better than existing topic models on real datasets. |