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Jinggui Liang

6 accepted papers

2025

Colloquial Singaporean English Style Transfer with Fine-Grained Explainable Control

ACL 2025long

Colloquial Singaporean English (Singlish) is an informal English marked by a unique blend of languages reflecting Singapore’s multicultural identity. Style transfer between Singlish and Standard (formal) English is vital for various applications, yet existing methods often lack explainability and fi…

Cited by 0SourcePDFScholar
2025

IntentionFrame: A Semi-Structured, Multi-Aspect Framework for Fine-Grained Conversational Intention Understanding

EMNLP 2025

Understanding user intentions in multi-turn dialogues is critical for conversational AI, yet existing approaches—relying on rigid slot-value structures or unstructured free-text—fail to fully capture conversational complexity. In this paper, we propose IntentionFrame, a semi-structured framework ins

Cited by 0SourcePDFScholar
2024

A Survey of Ontology Expansion for Conversational Understanding

EMNLP 2024main

In the rapidly evolving field of conversational AI, Ontology Expansion (OnExp) is crucial for enhancing the adaptability and robustness of conversational agents. Traditional models rely on static, predefined ontologies, limiting their ability to handle new and unforeseen user needs. This survey pape…

Cited by 0SourcePDFScholar
2024

Actively Learn from LLMs with Uncertainty Propagation for Generalized Category Discovery

NAACL 2024long

Generalized category discovery faces a key issue: the lack of supervision for new and unseen data categories. Traditional methods typically combine supervised pretraining with self-supervised learning to create models, and then employ clustering for category identification. However, these approaches…

2024

Synergizing Large Language Models and Pre-Trained Smaller Models for Conversational Intent Discovery

ACL 2024findings

In Conversational Intent Discovery (CID), Small Language Models (SLMs) struggle with overfitting to familiar intents and fail to label newly discovered ones. This issue stems from their limited grasp of semantic nuances and their intrinsically discriminative framework. Therefore, we propose Synergiz…