Papers by Bo Zong
Temporal Knowledge Graph Reasoning with Dynamic Hypergraph Embedding (2024.lrec-main)
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| Challenge: | Existing models that model temporal dynamics with knowledge graphs and graph convolution networks lack high-order interactions between objects in TKG, which is an important factor to predict future facts. |
| Approach: | They propose to embed temporal knowledge graph reasoning by constructing hypergraphs based on temporal information graphs at different timestamps and then adapt dynamic meta-embedding to fit TKG. |
| Outcome: | The proposed method outperforms baseline models on public TKG datasets and provides good interpretation for the predicted results. |
AT²PO: Agentic Turn-based Policy Optimization via Tree Search (2026.acl-long)
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| Challenge: | Recent advances in large language models (LLMs) have catalyzed the development of autonomous agents capable of executing complex, multi-turn tasks. |
| Approach: | They propose a framework for agentic reinforcement learning that integrates turn-level tree search with tree search to address key challenges. |
| Outcome: | The proposed framework addresses key challenges: limited exploration diversity, sparse credit assignment, and misaligned policy optimization. |
Unsupervised Concept Representation Learning for Length-Varying Text Similarity (2021.naacl-main)
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| Challenge: | Existing document similarity approaches suffer from the information gap caused by context and vocabulary mismatches when comparing varying-length texts. |
| Approach: | They propose an unsupervised concept representation learning approach to address this issue . they propose a concept-based document matching method to leverage recognition of local phrase features . |
| Outcome: | The proposed method achieves a better F1 score than baseline models on real-world data sets. |
RealMedDial: A Real Telemedical Dialogue Dataset Collected from Online Chinese Short-Video Clips (2022.coling-1)
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| Challenge: | Existing medical dialogue systems are limited by the lack of corpora and data from real scenarios. |
| Approach: | They construct a Chinese medical dialogue dataset based on real medical consultations. |
| Outcome: | The proposed dataset is applicable to a wide range of NLP tasks with respect to medical dialogue. |
HyperHatePrompt: A Hypergraph-based Prompting Fusion Model for Multimodal Hate Detection (2025.coling-main)
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| Challenge: | Existing models for multimodal hate detection lack implicit hateful cues, cross-modal-induced hate, and diversity of hate target groups. |
| Approach: | They propose a hypergraph-based prompting fusion model that uses LLMs to generate hate cue prompts and hypergraph learning to merge multimodal hate features. |
| Outcome: | The proposed model outperforms state-of-the-art models on two benchmark datasets showing that it can detect hate content across multiple modalities. |
Conditional Semantic Textual Similarity via Conditional Contrastive Learning (2025.coling-main)
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| Challenge: | Existing methods to assess similarity between sentences encounter over-estimation problem . compared to fuzzy representations, similarity is comparatively lower in terms of "The person's age". |
| Approach: | They propose a conditional contrastive learning framework that constructs positive and negative samples from two perspectives. |
| Outcome: | The proposed method achieves state-of-the-art performance with five models based on bi-encoder and tri-encoding architectures. |
Improve Meta-learning for Few-Shot Text Classification with All You Can Acquire from the Tasks (2024.findings-emnlp)
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| Challenge: | Existing methods for few-shot text classification often encounter problems drawing accurate class prototypes from support set samples. |
| Approach: | They propose a meta-learning method that leverages the information within the task itself . they propose Query-Data-Augmenter and Label-Adapter to build a task-adaptive metric space . |
| Outcome: | The proposed method shows obvious advantages over state-of-the-art models on eight benchmark datasets. |
RENN: A Rule Embedding Enhanced Neural Network Framework for Temporal Knowledge Graph Completion (2024.lrec-main)
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| Challenge: | Existing methods for temporal knowledge graph embedding do not account for structural dependencies between relations. |
| Approach: | They propose a framework that enhances temporal knowledge graph completion through rule embedding. |
| Outcome: | The proposed framework improves temporal knowledge graph completion through rule embedding. |
Unveiling Opinion Evolution via Prompting and Diffusion for Short Video Fake News Detection (2024.findings-acl)
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| Challenge: | Existing methods for short video fake news detection ignore the implicit opinions and evolving nature of opinions across modalities. |
| Approach: | They propose a short video fake news model that mines implicit opinions within short videos and promotes the evolution of both explicit and implicit opinions across all modalities. |
| Outcome: | The proposed model outperforms existing methods on a publicly available dataset for short video fake news detection. |