Papers with hierarchical modeling
CoMAE: A Multi-factor Hierarchical Framework for Empathetic Response Generation (2021.findings-acl)
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| Challenge: | Existing methods for empathetic response generation ignore hierarchical relationships between different factors, leading to a weak ability of empathy modeling. |
| Approach: | They propose a multi-factor hierarchical framework for empathetic response generation which models the above three key factors in a hierarchically structured way. |
| Outcome: | The proposed model generates more empathetic responses than previous methods. |
HYDEN: Hyperbolic Density Representations for Medical Images and Reports (2025.coling-main)
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| Challenge: | Existing methods for visual semantic representation learning struggle to address semantic uncertainty, especially in the medical domain. |
| Approach: | They propose a hyperbolic density embedding based image-text representation learning approach tailored for specific medical domain data. |
| Outcome: | The proposed method performs better than baseline methods on zero-shot tasks and fine-tuning tasks on different datasets. |
From Coarse to Fine: A Multi-Granularity Multimodal Framework for Teacher Sentiment Analysis (2026.findings-acl)
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| Challenge: | Existing approaches to teacher sentiment analysis treat it as a static label . current approaches fail to capture structured heterogeneity of classroom expressions . |
| Approach: | They propose a coarse-to-fine multimodal framework that decomposes teacher sentiment into three granularities and employ CLS-guided cross-modal attention to recover effective signals from regulated displays. |
| Outcome: | The proposed framework outperforms state-of-the-art models on T-MED and CMU-MOSEI. |
Global and Local Hierarchical Prompt Tuning Framework for Multi-level Implicit Discourse Relation Recognition (2024.lrec-main)
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| Challenge: | Recent methods to recognize hierarchical discourse relations without explicit connectives are inefficient and ignore the utilization of the output probability distribution information of the verbalizer. |
| Approach: | They propose a global and local hierarchical prompt tuning framework which leverages top-up propagated probability as the global hierarchy to inject it into multi-level verbalizer. |
| Outcome: | The proposed framework achieves competitive results on two benchmacks. |
KOLD: Korean Offensive Language Dataset (2022.emnlp-main)
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| Challenge: | Recent directions for offensive language detection focus on English and do not transfer well to other languages because of cultural and linguistic differences. |
| Approach: | They present a Korean offensive language dataset annotated with offensive language comments . they use the comments as training data for Korean BERT and RoBERTa models . |
| Outcome: | The proposed model improves offensiveness detection, target classification, and span detection while having room for improvement for target group classification and span prediction. |