Papers by Khac-Hoai Nam Bui
SynTOD: Augmented Response Synthesis for Robust End-to-End Task-Oriented Dialogue System (2024.lrec-main)
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| Challenge: | Task-oriented dialogue systems focus on training multiple tasks such as language understanding, tracking states, and generating appropriate responses to help users achieve their specific goals. |
| Approach: | They exploit the ability of pre-trained models to provide synthesis responses for fine-tuning end-to-end TOD systems. |
| Outcome: | The proposed model outperforms baseline models on multiwoz datasets and is available for further exploitation. |
CollabCoder: Plan-Code Co-Evolution via Collaborative Decision-Making for Efficient Code Generation (2026.findings-acl)
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| Challenge: | Existing multi-agent code generation frameworks are constrained by static planning, isolated execution, high computational overhead, and limited adaptability to complex tasks. |
| Approach: | They propose a plan-code co-evolution framework that allows dynamic multi-agent collaboration to improve code quality and robustness across tasks. |
| Outcome: | The proposed framework improves code quality and robustness across tasks while reducing the number of API calls by an average of 4-10 per execution. |
HeterGraphLongSum: Heterogeneous Graph Neural Network with Passage Aggregation for Extractive Long Document Summarization (2022.coling-1)
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| Challenge: | Existing models for extractive document summarization are based on sequence-to-sequence (Seq2Sequency) but long-form document summaries using graph-based methods are still an open research issue. |
| Approach: | They propose a heterogeneous graph neural network model to improve the performance of extractive document summarization using graph-based methods. |
| Outcome: | The proposed model can achieve state-of-the-art performance without pre-trained language models. |
ClaimPKG: Enhancing Claim Verification via Pseudo-Subgraph Generation with Lightweight Specialized LLM (2025.findings-acl)
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| Challenge: | Existing verification methods rely on unstructured text corpora to break down claims . despite strong reasoning abilities, modern LLMs struggle with modular pipelines . |
| Approach: | They propose a framework that integrates knowledge graphs with LLM reasoning . they propose KGs provide structured, semantically rich representations . |
| Outcome: | The proposed framework outperforms baselines on the FactKG dataset by 9%-12% accuracy points across multiple categories. |
Verify-in-the-Graph: Entity Disambiguation Enhancement for Complex Claim Verification with Interactive Graph Representation (2025.naacl-long)
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| Challenge: | Existing approaches to claim verification are based on decomposing claims into sub-claims and querying a knowledge base to resolve hidden or ambiguous entities. |
| Approach: | They propose a framework that leverages the reasoning and comprehension abilities of LLM agents to solve ambiguous entities in a graph. |
| Outcome: | The proposed framework achieves competitive performance compared to baselines across benchmarks. |
Multi Graph Neural Network for Extractive Long Document Summarization (2022.coling-1)
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| Challenge: | Heterogeneous Graph Neural Networks (GNN) have been proposed as an emergent approach for extracting document summarization (EDS) but there are still limitations in applying it for long documents due to the lack of inter-sentence connections. |
| Approach: | They propose to build a graph on sentence-level nodes and combine it with HeterGNN to capture the semantic information in terms of both inter and intra-sentence connections. |
| Outcome: | Experiments on two datasets show that the proposed method achieves state-of-the-art in this research field. |
KG-CQR: Leveraging Structured Relation Representations in Knowledge Graphs for Contextual Query Retrieval (2025.emnlp-main)
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| Challenge: | Existing methods that address corpus-level context loss focus on query enrichment through structured relation representations. |
| Approach: | They propose a framework for Contextual Query Retrieval that enriches queries with contextual representations derived from a corpus-centric KG. |
| Outcome: | The proposed framework outperforms strong baselines on RAGBench and MultiHop-RAG datasets in terms of retrieval effectiveness. |