Papers with probabilistic modeling
Probabilistic Embedding of Knowledge Graphs with Box Lattice Measures (P18-1)
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| Challenge: | Structured embeddings based on regions, densities, and orderings have gained popularity for their inductive bias towards the essential asymmetries inherent in problems such as image captioning. |
| Approach: | They propose a box lattice and accompanying probability measure to capture negative correlations over arbitrary concepts. |
| Outcome: | The proposed model can capture anti-correlation and even disjoint concepts while learning from and predicting calibrated uncertainty. |
MERLIN: Multimodal Embedding Refinement via LLM-based Iterative Navigation for Text-Video Retrieval-Rerank Pipeline (2024.emnlp-industry)
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| Challenge: | Recent advances in text-video retrieval neglect the crucial user perspective, leading to discrepancies between user queries and content retrieved. |
| Approach: | They propose a novel, training-free pipeline that leverages Large Language Models for iterative feedback learning. |
| Outcome: | Experimental results show that MERLIN significantly outperforms existing systems in video retrieval. |
BERT-Beta: A Proactive Probabilistic Approach to Text Moderation (2021.emnlp-main)
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| Challenge: | Existing approaches to text moderation are reactive and do not account for user generated content. |
| Approach: | They propose a text toxicity propensity model to characterize extent to which a user generated text attracts toxic comments and introduce a beta regression model to do the probabilistic modeling. |
| Outcome: | The proposed model performs well in comprehensive experiments and is scalable. |
Probabilistic Depression Detection from Textual Time Series (2026.findings-acl)
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| Challenge: | Existing models for depression severity estimations lack uncertainty estimates and temporal interpretability. |
| Approach: | They propose a Probabilistic framework for Depression Detection from clinical interview utterance sequences that predicts PHQ-8 scores while modeling calibrated uncertainty. |
| Outcome: | The proposed framework achieves competitive performance among text-only systems and produces well-calibrated intervals. |
Multi-Task Representation Alignment on Language Understanding: A Mutual Information Perspective (2026.acl-long)
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| Challenge: | Existing approaches to multitask learning fail to address task interference issues . Existing methods focus on task balancing or probabilistic modeling but fail to learn sufficient representations for all target tasks. |
| Approach: | They propose a multi-task representation alignment framework to achieve task-specific alignment and self-alignment on shared representations from a mutual information perspective. |
| Outcome: | The proposed framework outperforms 13 representative MTL methods under label-noisy and data-constrained conditions. |