no code implementations • 26 Jan 2024 • Nan Hu, Jiaoyan Chen, Yike Wu, Guilin Qi, Sheng Bi, Tongtong Wu, Jeff Z. Pan
The attribution of question answering is to provide citations for supporting generated statements, and has attracted wide research attention.
1 code implementation • 20 Sep 2023 • Yike Wu, Nan Hu, Sheng Bi, Guilin Qi, Jie Ren, Anhuan Xie, Wei Song
To this end, we propose an answer-sensitive KG-to-Text approach that can transform KG knowledge into well-textualized statements most informative for KGQA.
1 code implementation • 8 Jun 2023 • Yue Zhen, Sheng Bi, Lu Xing-tong, Pan Wei-qin, Shi Hai-peng, Chen Zi-rui, Fang Yi-shu
Traditional robot task planning methods face challenges when dealing with highly unstructured environments and complex tasks.
no code implementations • Findings (EMNLP) 2021 • Sheng Bi, Xiya Cheng, Yuan-Fang Li, Lizhen Qu, Shirong Shen, Guilin Qi, Lu Pan, Yinlin Jiang
The ability to generate natural-language questions with controlled complexity levels is highly desirable as it further expands the applicability of question generation.
no code implementations • Findings (ACL) 2021 • Shirong Shen, Tongtong Wu, Guilin Qi, Yuan-Fang Li, Gholamreza Haffari, Sheng Bi
Event detection (ED) aims at detecting event trigger words in sentences and classifying them into specific event types.
no code implementations • 8 Mar 2021 • Michael McEldrew, Zachary A. H. Goodwin, Sheng Bi, Alexei A. Kornyshev, Martin Z. Bazant
Our model is able to quantitatively reproduce the populations of ionic clusters of different sizes as a function of salt concentration, the critical salt concentration for ionic gelation, and the fraction of ions incorporated into the ionic gel, as observed from molecular simulations of three different lithium-based WiSEs.
Chemical Physics Soft Condensed Matter Statistical Mechanics
no code implementations • COLING 2020 • Shirong Shen, Guilin Qi, Zhen Li, Sheng Bi, Lusheng Wang
We label a Chinese legal event dataset and evaluate our model on it.
no code implementations • COLING 2020 • Sheng Bi, Xiya Cheng, Yuan-Fang Li, Yongzhen Wang, Guilin Qi
Question generation over knowledge bases (KBQG) aims at generating natural-language questions about a subgraph, i. e. a set of (connected) triples.
no code implementations • 7 Oct 2020 • Xiya Cheng, Sheng Bi, Guilin Qi, Yongzhen Wang
In this paper, we propose a knowledge-attentive neural network model, which introduces legal schematic knowledge about charges and exploit the knowledge hierarchical representation as the discriminative features to differentiate confusing charges.