1 code implementation • 28 Feb 2023 • Kangyang Luo, Xiang Li, Yunshi Lan, Ming Gao
Federated Learning (FL) has emerged as a de facto machine learning area and received rapid increasing research interests from the community.
1 code implementation • 3 Sep 2022 • Yunshi Lan, Lei Wang, Jing Jiang, Ee-Peng Lim
To improve the compositional generalization in MWP solving, we propose an iterative data augmentation method that includes diverse compositional variation into training data and could collaborate with MWP methods.
1 code implementation • 2 Sep 2021 • Yihuai Lan, Lei Wang, Qiyuan Zhang, Yunshi Lan, Bing Tian Dai, Yan Wang, Dongxiang Zhang, Ee-Peng Lim
Over the last few years, there are a growing number of datasets and deep learning-based methods proposed for effectively solving MWPs.
Ranked #6 on Math Word Problem Solving on Math23K
1 code implementation • 15 Aug 2021 • Yunshi Lan, Gaole He, Jinhao Jiang, Jing Jiang, Wayne Xin Zhao, Ji-Rong Wen
Knowledge base question answering (KBQA) aims to answer a question over a knowledge base (KB).
1 code implementation • ACL 2021 • Yunshi Lan, Jing Jiang
We propose a novel graph-based model to capture the transitions of focal entities and apply a graph neural network to derive a probability distribution of focal entities for each question, which is then combined with a standard KBQA module to perform answer ranking.
1 code implementation • Findings (NAACL) 2022 • Zhenwen Liang, Jipeng Zhang, Lei Wang, Wei Qin, Yunshi Lan, Jie Shao, Xiangliang Zhang
Math word problem (MWP) solving faces a dilemma in number representation learning.
Ranked #3 on Math Word Problem Solving on MathQA
no code implementations • 25 May 2021 • Yunshi Lan, Gaole He, Jinhao Jiang, Jing Jiang, Wayne Xin Zhao, Ji-Rong Wen
In this paper, we elaborately summarize the typical challenges and solutions for complex KBQA.
1 code implementation • 11 Jan 2021 • Gaole He, Yunshi Lan, Jing Jiang, Wayne Xin Zhao, Ji-Rong Wen
In our approach, the student network aims to find the correct answer to the query, while the teacher network tries to learn intermediate supervision signals for improving the reasoning capacity of the student network.
Ranked #2 on Semantic Parsing on WebQuestionsSP
no code implementations • ACL 2020 • Yunshi Lan, Jing Jiang
Previous work on answering complex questions from knowledge bases usually separately addresses two types of complexity: questions with constraints and questions with multiple hops of relations.
no code implementations • 20 Jan 2020 • Shuohang Wang, Yunshi Lan, Yi Tay, Jing Jiang, Jingjing Liu
Transformer has been successfully applied to many natural language processing tasks.
no code implementations • COLING 2018 • Yunshi Lan, Jing Jiang
In this paper, we study how we can improve a deep learning approach to textual entailment by incorporating lexical entailment relations from WordNet.