Papers by Minh-Tien Nguyen
Enhance Incomplete Utterance Restoration by Joint Learning Token Extraction and Text Generation (2022.naacl-main)
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| Challenge: | omitted tokens from the context contribute to incomplete utterance restoration (IUR) understanding conversational interactions through NLP has become important with increasing connectivity and range of capabilities. |
| Approach: | They propose a model for incomplete utterance restoration called JET . they construct a Picker that identifies omitted tokens and two label creation methods to support the picker. |
| Outcome: | The proposed model is better than pretrained T5 and non-generative language model methods on four benchmark datasets in extraction and abstraction scenarios. |
Metamo: Empowering Large Language Models with Psychological Distortion Detection for Cognition-aware Coaching (2025.emnlp-demos)
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| Challenge: | Metamo is a browser-based dialogue system that transforms an off-the-shelf large language model into an empathetic coach for everyday workplace concerns. |
| Approach: | They propose a browser-based dialogue system that first identifies the cognitive distortion behind an emotion, then recognizes the user’s emotion, and finally produces a question-centered reply that invites reflection. |
| Outcome: | Empirical tests on public corpora showed that the proposed system improved emotionrecognition quality and response diversity without sacrificing latency. |
CMTD: Cognitive Modeling with Traits and Distortions for Multimodal Emotion Recognition in Conversations (2026.findings-acl)
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| Challenge: | Experimental results show that traits temper negativity bias from distortions, and cognitive modeling with psychological, visual, and acoustic information can improve the performance of MERC. |
| Approach: | They propose a framework for multimodal emotion recognition in conversations that takes advantage of stable personality traits, dynamic cognitive distortions, visual and acoustic features of interlocutors to enhance the emotional intelligence of LLMs. |
| Outcome: | Experimental results show that traits temper negativity bias from distortions, and cognitive modeling with psychological, visual, and acoustic information can improve the performance of MERC. |
Meeting Decision Tracker: Making Meeting Minutes with De-Contextualized Utterances (2022.aacl-demo)
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| Challenge: | Existing systems to itemize meetings' decisions are lacking in their raw form due to utterance collapse. |
| Approach: | They propose a prototype system to construct decision items that deal with utterance collapse in natural conversation. |
| Outcome: | The proposed system improves the user experience by dealing with utterance collapse in natural conversation. |
TSix: A Human-involved-creation Dataset for Tweet Summarization (L18-1)
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| Challenge: | a new dataset for tweet summarization is available for free. |
| Approach: | They propose a dataset for tweet summarization that uses human annotations to evaluate extractive summarizing methods. |
| Outcome: | The proposed dataset includes six events collected from Twitter . human-annotated gold-standard references facilitate evaluation, the study shows . |
ViLegalLM: Language Models for Vietnamese Legal Text (2026.findings-acl)
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| Challenge: | **ViLegalLM** is the first suite of Vietnamese pretrained language models for legal text understanding and generation. |
| Approach: | They propose a suite of Vietnamese pretrained language models for legal text understanding and generation. |
| Outcome: | The proposed models outperform instruction-tuned adaptation on four main Vietnamese legal downstream tasks. |
SuperRAG: Beyond RAG with Layout-Aware Graph Modeling (2025.naacl-industry)
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| Challenge: | Existing methods that only deal with flat text chunks use a graph structure to handle complex questions. |
| Approach: | They propose layout-aware graph modeling for multimodal RAG using document layout parsing to take into account relationship of multimodalities. |
| Outcome: | The proposed method can handle complex questions that require information from multimodalities. |
Towards Safer Operations: An Expert-involved Dataset of High-Pressure Gas Incidents for Preventing Future Failures (2023.emnlp-industry)
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| Challenge: | Existing datasets for incident management tasks are labor-intensive and time-consuming. |
| Approach: | They propose a new IncidentAI dataset for safety prevention that includes three tasks . they argue that NLP techniques are beneficial for analyzing incident reports . |
| Outcome: | The proposed dataset shows that NLP techniques are beneficial for analyzing incident reports to prevent future failures. |
Make The Most of Prior Data: A Solution for Interactive Text Summarization with Preference Feedback (2022.findings-naacl)
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Duy-Hung Nguyen, Nguyen Viet Dung Nghiem, Bao-Sinh Nguyen, Dung Tien Tien Le, Shahab Sabahi, Minh-Tien Nguyen, Hung Le
| Challenge: | a framework to train summarization models with preference feedback is proposed . human-in-the-loop (HITL) allows humans to actively participate in supervising AI systems . |
| Approach: | They propose a framework to train summarization models with preference feedback interactively. |
| Outcome: | The proposed framework improves ROUGE scores and sample-efficiency in active, few-shot and online settings. |