Share This Author
Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog
This work investigates methods that can make explicit use of domain knowledge and introduce a shared-private network to learn shared and specific knowledge and proposes a novel Dynamic Fusion Network (DF-Net) which automatically exploit the relevance between the target domain and each domain.
AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot Filling
An Adaptive Graph-Interactive Framework for joint multiple intent detection and slot filling is proposed, where an intent-slot graph interaction layer is applied to each token adaptively to model the strong correlation between the slot and intents.
Don’t be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System
- Libo Qin, Tianbao Xie, Shijue Huang, Qiguang Chen, Xiao Xu, Wanxiang Che
- Computer ScienceEMNLP
- 23 September 2021
This paper introduces CI-ToD, a novel dataset for Consistency Identification in Task-oriented Dialog system, and annotates the single label to enable the model to judge whether the system response is contradictory, but also provides more fine-grained labels to encourage model to know what inconsistent sources lead to it.
TD-GIN: Token-level Dynamic Graph-Interactive Network for Joint Multiple Intent Detection and Slot Filling
This paper proposes a Token-level Dynamic Graph-Interactive Network (TD-GIN) for joint multiple intent detection and slot filling, where it model the interaction between multiple intents and each token slot in a unified graph architecture.
IPGAN: Generating Informative Item Pairs by Adversarial Sampling
- G. Guo, Huan Zhou, Xiuqiang He
- Computer ScienceIEEE Transactions on Neural Networks and Learning…
- 27 October 2020
A dynamic sampling strategy to search informative item pairs with positive and negative instances is introduced and a batch-training approach is proposed to further enhance both user and item modeling by alleviating the special bias (noise) from different users.
GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling
This paper proposes a non-autoregressive model for joint multiple intent detection and slot filling, achieving more fast and accurate performance while being 11.5 times faster.
RNA binding protein-based risk score model for prognosis prediction of patients with hepatocellular carcinoma.
Bridge-Tower: Building Bridges Between Encoders in Vision-Language Representation Learning
This paper introduces multiple bridge layers that build a connection between the top layers of uni-modal encoders and each layer of the cross- modal encoder, which enables comprehensive bottom-up interactions between visual and textual representations at different semantic levels, resulting in more effective cross-Modal alignment and fusion.
Text is no more Enough! A Benchmark for Profile-based Spoken Language Understanding
- Xiao Xu, Libo Qin, Kaiji Chen, Guoxing Wu, Linlin Li, Wanxiang Che
- Computer ScienceAAAI
- 22 December 2021
This paper introduces a new and important task, Profile-based Spoken Language Understanding (ProSLU), which requires the model that not only relies on the plain text but also the supporting profile information to predict the correct intents and slots, and introduces a large-scale human-annotated Chinese dataset with over 5K utterances.