69 citations · 138 across the 32 of their papers we have counts for
9 papers · 2 filters
End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions
Libo Qin, Wenbo Pan, Qiguang Chen +5
End-to-end task-oriented dialogue (EToD) can directly generate responses in an end-to-end fashion without modular training, which attracts escalating popularity. The advancement of…
Cross-lingual Prompting: Improving Zero-shot Chain-of-Thought Reasoning across Languages
Libo Qin, Qiguang Chen, Fuxuan Wei +2
Chain-of-thought (CoT) is capable of eliciting models to explicitly generate reasoning paths, thus promoting reasoning accuracy and attracting increasing attention. Specifically, z…
Improving Few-shot and Zero-shot Entity Linking with Coarse-to-Fine Lexicon-based Retriever
Shijue Huang, Bingbing Wang, Libo Qin +2
Few-shot and zero-shot entity linking focus on the tail and emerging entities, which are more challenging but closer to real-world scenarios. The mainstream method is the ''retriev…
MMSD2.0: Towards a Reliable Multi-modal Sarcasm Detection System
Libo Qin, Shijue Huang, Qiguang Chen +5
Multi-modal sarcasm detection has attracted much recent attention. Nevertheless, the existing benchmark (MMSD) has some shortcomings that hinder the development of reliable multi-m…
OpenSLU: A Unified, Modularized, and Extensible Toolkit for Spoken Language Understanding
Libo Qin, Qiguang Chen, Xiao Xu +2
Spoken Language Understanding (SLU) is one of the core components of a task-oriented dialogue system, which aims to extract the semantic meaning of user queries (e.g., intents and…
LasUIE: Unifying Information Extraction with Latent Adaptive Structure-aware Generative Language Model
Hao Fei, Shengqiong Wu, Jingye Li +6
Universally modeling all typical information extraction tasks (UIE) with one generative language model (GLM) has revealed great potential by the latest study, where various IE pred…
A Preliminary Evaluation of ChatGPT for Zero-shot Dialogue Understanding
Wenbo Pan, Qiguang Chen, Xiao Xu +2
Zero-shot dialogue understanding aims to enable dialogue to track the user's needs without any training data, which has gained increasing attention. In this work, we investigate th…
A Two-Stage Framework with Self-Supervised Distillation For Cross-Domain Text Classification
Yunlong Feng, Bohan Li, Libo Qin +2
Cross-domain text classification aims to adapt models to a target domain that lacks labeled data. It leverages or reuses rich labeled data from the different but related source dom…
HIT-SCIR at MMNLU-22: Consistency Regularization for Multilingual Spoken Language Understanding
Bo Zheng, Zhouyang Li, Fuxuan Wei +3
Multilingual spoken language understanding (SLU) consists of two sub-tasks, namely intent detection and slot filling. To improve the performance of these two sub-tasks, we propose…