1 code implementation • ACL (dialdoc) 2021 • Dingmin Wang, Ziyao Chen, Wanwei He, Li Zhong, Yunzhe Tao, Min Yang
Most existing neural network based task-oriented dialog systems follow encoder-decoder paradigm, where the decoder purely depends on the source texts to generate a sequence of words, usually suffering from instability and poor readability.
Ranked #9 on Task-Oriented Dialogue Systems on KVRET
1 code implementation • NeurIPS 2023 • Yeyuan Chen, Dingmin Wang
Moreover, we extend our analysis of expressiveness and graph transformation to temporal graphs, exploring several temporal GNN architectures and providing an expressiveness hierarchy for them.
2 code implementations • 30 Apr 2023 • Dongyu Gong, Xingchen Wan, Dingmin Wang
Working memory is a critical aspect of both human intelligence and artificial intelligence, serving as a workspace for the temporary storage and manipulation of information.
1 code implementation • 15 Aug 2022 • Dingmin Wang, Przemysław Andrzej Wałęga, Bernardo Cuenca Grau
DatalogMTL is an extension of Datalog with metric temporal operators that has found applications in temporal ontology-based data access and query answering, as well as in stream reasoning.
1 code implementation • 1 Jun 2022 • Dingmin Wang, Shengchao Liu, Hanchen Wang, Bernardo Cuenca Grau, Linfeng Song, Jian Tang, Song Le, Qi Liu
Graph Neural Networks (GNNs) are effective tools for graph representation learning.
1 code implementation • 12 Jan 2022 • Dingmin Wang, Pan Hu, Przemysław Andrzej Wałęga, Bernardo Cuenca Grau
DatalogMTL is an extension of Datalog with operators from metric temporal logic which has received significant attention in recent years.
1 code implementation • 10 Jun 2021 • Dingmin Wang, Ziyao Chen, Wanwei He, Li Zhong, Yunzhe Tao, Min Yang
Most existing neural network based task-oriented dialogue systems follow encoder-decoder paradigm, where the decoder purely depends on the source texts to generate a sequence of words, usually suffering from instability and poor readability.
no code implementations • NAACL 2021 • Dingmin Wang, Chenghua Lin, Qi Liu, Kam-Fai Wong
We present a fast and scalable architecture called Explicit Modular Decomposition (EMD), in which we incorporate both classification-based and extraction-based methods and design four modules (for classification and sequence labelling) to jointly extract dialogue states.
no code implementations • ACL 2019 • Dingmin Wang, Yi Tay, Li Zhong
This paper proposes Confusionset-guided Pointer Networks for Chinese Spell Check (CSC) task.
1 code implementation • EMNLP 2018 • Dingmin Wang, Yan Song, Jing Li, Jialong Han, Haisong Zhang
Chinese spelling check (CSC) is a challenging yet meaningful task, which not only serves as a preprocessing in many natural language processing(NLP) applications, but also facilitates reading and understanding of running texts in peoples{'} daily lives.
no code implementations • WS 2017 • Gabriel Fung, Maxime Debosschere, Dingmin Wang, Bo Li, Jia Zhu, Kam-Fai Wong
This paper provides an overview along with our findings of the Chinese Spelling Check shared task at NLPTEA 2017.