no code implementations • 15 Apr 2024 • Sanxing Chen, Sam Wiseman, Bhuwan Dhingra
The desire and ability to seek new information strategically are fundamental to human learning but often overlooked in current language agent development.
no code implementations • 20 Jan 2024 • Yu Bai, Heyan Huang, Cesare Spinoso-Di Piano, Marc-Antoine Rondeau, Sanxing Chen, Yang Gao, Jackie Chi Kit Cheung
In-context learning (ICL) has become an effective solution for few-shot learning in natural language processing.
1 code implementation • 31 Mar 2023 • Sanxing Chen, Yongqiang Chen, Börje F. Karlsson
Temporal and numerical expression understanding is of great importance in many downstream Natural Language Processing (NLP) and Information Retrieval (IR) tasks.
no code implementations • 28 Mar 2023 • Sanxing Chen, Hao Cheng, Xiaodong Liu, Jian Jiao, Yangfeng Ji, Jianfeng Gao
Learning transferable representation of knowledge graphs (KGs) is challenging due to the heterogeneous, multi-relational nature of graph structures.
1 code implementation • COLING 2020 • Sanxing Chen, Aidan San, Xiaodong Liu, Yangfeng Ji
In Text-to-SQL semantic parsing, selecting the correct entities (tables and columns) for the generated SQL query is both crucial and challenging; the parser is required to connect the natural language (NL) question and the SQL query to the structured knowledge in the database.
3 code implementations • EMNLP 2021 • Sanxing Chen, Xiaodong Liu, Jianfeng Gao, Jian Jiao, Ruofei Zhang, Yangfeng Ji
Our proposed model consists of two different Transformer blocks: the bottom block extracts features of each entity-relation pair in the local neighborhood of the source entity and the top block aggregates the relational information from outputs of the bottom block.
Ranked #1 on Link Prediction on FB15k-237 (Hit@10 metric)