21 citations · 57 across the 17 of their papers we have counts for
9 papers · 1 filter
Pro-HAN: A Heterogeneous Graph Attention Network for Profile-Based Spoken Language Understanding
Dechuan Teng, Chunlin Lu, Xiao Xu +2
Recently, Profile-based Spoken Language Understanding (SLU) has gained increasing attention, which aims to incorporate various types of supplementary profile information (i.e., Kno…
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…
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…
Text is no more Enough! A Benchmark for Profile-based Spoken Language Understanding
Xiao Xu, Libo Qin, Kaiji Chen +3
Current researches on spoken language understanding (SLU) heavily are limited to a simple setting: the plain text-based SLU that takes the user utterance as input and generates its…
Don't be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System
Libo Qin, Tianbao Xie, Shijue Huang +3
Consistency Identification has obtained remarkable success on open-domain dialogue, which can be used for preventing inconsistent response generation. However, in contrast to the r…
GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling
Libo Qin, Fuxuan Wei, Tianbao Xie +3
Multi-intent SLU can handle multiple intents in an utterance, which has attracted increasing attention. However, the state-of-the-art joint models heavily rely on autoregressive ap…
Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog
Libo Qin, Xiao Xu, Wanxiang Che +2
Recent studies have shown remarkable success in end-to-end task-oriented dialog system. However, most neural models rely on large training data, which are only available for a cert…
AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot Filling
Libo Qin, Xiao Xu, Wanxiang Che +1
In real-world scenarios, users usually have multiple intents in the same utterance. Unfortunately, most spoken language understanding (SLU) models either mainly focused on the sing…