no code implementations • 22 Feb 2024 • Qi Hu, Weifeng Jiang, Haoran Li, ZiHao Wang, Jiaxin Bai, Qianren Mao, Yangqiu Song, Lixin Fan, JianXin Li
An entity can be involved in various knowledge graphs and reasoning on multiple KGs and answering complex queries on multi-source KGs is important in discovering knowledge cross graphs.
1 code implementation • 14 Jan 2024 • Weiqi Wang, Tianqing Fang, Chunyang Li, Haochen Shi, Wenxuan Ding, Baixuan Xu, Zhaowei Wang, Jiaxin Bai, Xin Liu, Jiayang Cheng, Chunkit Chan, Yangqiu Song
The sequential process of conceptualization and instantiation is essential to generalizable commonsense reasoning as it allows the application of existing knowledge to unfamiliar scenarios.
no code implementations • 25 Dec 2023 • Jiaxin Bai, Yicheng Wang, Tianshi Zheng, Yue Guo, Xin Liu, Yangqiu Song
Although many applications require the use of knowledge for explanations, the utilization of abductive reasoning in conjunction with structured knowledge, such as a knowledge graph, remains largely unexplored.
no code implementations • 25 Dec 2023 • Qi Hu, Haoran Li, Jiaxin Bai, ZiHao Wang, Yangqiu Song
Neural graph databases (NGDBs) have emerged as a powerful paradigm that combines the strengths of graph databases (GDBs) and neural networks to enable efficient storage, retrieval, and analysis of graph-structured data which can be adaptively trained with LLMs.
no code implementations • 21 Dec 2023 • Jiaxin Bai, Chen Luo, Zheng Li, Qingyu Yin, Yangqiu Song
In this paper, we introduce the task of logical session complex query answering, where sessions are treated as hyperedges of items, and we formulate the problem of complex intention understanding as a task of logical session complex queries answering (LS-CQA) on an aggregated hypergraph of sessions, items, and attributes.
1 code implementation • 2 Jun 2023 • Jiaxin Bai, Chen Luo, Zheng Li, Qingyu Yin, Bing Yin, Yangqiu Song
To address the difference between entities and numerical values, we also propose the framework of Number Reasoning Network (NRN) for alternatively encoding entities and numerical values into separate encoding structures.
1 code implementation • NeurIPS 2023 • Jiaxin Bai, Xin Liu, Weiqi Wang, Chen Luo, Yangqiu Song
Traditional neural complex query answering (CQA) approaches mostly work on entity-centric KGs.
1 code implementation • 25 Feb 2023 • Jiaxin Bai, Tianshi Zheng, Yangqiu Song
Instead of parameterizing and executing the computational graph, SQE first uses a search-based algorithm to linearize the computational graph to a sequence of tokens and then uses a sequence encoder to compute its vector representation.
1 code implementation • 15 Nov 2022 • Changlong Yu, Weiqi Wang, Xin Liu, Jiaxin Bai, Yangqiu Song, Zheng Li, Yifan Gao, Tianyu Cao, Bing Yin
Understanding users' intentions in e-commerce platforms requires commonsense knowledge.
1 code implementation • Findings (NAACL) 2022 • Jiaxin Bai, ZiHao Wang, Hongming Zhang, Yangqiu Song
The query embedding method is proposed to answer these queries by jointly encoding queries and entities to the same embedding space.
no code implementations • 4 Sep 2021 • Zizheng Lin, Haowen Ke, Ngo-Yin Wong, Jiaxin Bai, Yangqiu Song, Huan Zhao, Junpeng Ye
To tackle this challenge, we develop a multi-relational graph based MTL model called Heterogeneous Multi-Task Graph Isomorphism Network (HMTGIN) which efficiently solves heterogeneous CQA tasks.
1 code implementation • EACL 2021 • Jiaxin Bai, Hongming Zhang, Yangqiu Song, Kun Xu
Character linking, the task of linking mentioned people in conversations to the real world, is crucial for understanding the conversations.
1 code implementation • IJCNLP 2019 • Hongming Zhang, Jiaxin Bai, Yan Song, Kun Xu, Changlong Yu, Yangqiu Song, Wilfred Ng, Dong Yu
Therefore, in this paper, we propose a multiplex word embedding model, which can be easily extended according to various relations among words.
7 code implementations • Findings of the Association for Computational Linguistics 2020 • Shizhe Diao, Jiaxin Bai, Yan Song, Tong Zhang, Yonggang Wang
Moreover, it is shown that reasonable performance can be obtained when ZEN is trained on a small corpus, which is important for applying pre-training techniques to scenarios with limited data.
Ranked #1 on Chinese Part-of-Speech Tagging on CTB5 Dev
Chinese Named Entity Recognition Chinese Word Segmentation +5