Zobrazeno 1 - 10
of 131
pro vyhledávání: '"Jiang, Shengyi"'
Currently, the majority of research in grammatical error correction (GEC) is concentrated on universal languages, such as English and Chinese. Many low-resource languages lack accessible evaluation corpora. How to efficiently construct high-quality e
Externí odkaz:
http://arxiv.org/abs/2410.20838
Autor:
Liu, Xu-Hui, Liu, Tian-Shuo, Jiang, Shengyi, Chen, Ruifeng, Zhang, Zhilong, Chen, Xinwei, Yu, Yang
Combining offline and online reinforcement learning (RL) techniques is indeed crucial for achieving efficient and safe learning where data acquisition is expensive. Existing methods replay offline data directly in the online phase, resulting in a sig
Externí odkaz:
http://arxiv.org/abs/2407.12448
Alphas are pivotal in providing signals for quantitative trading. The industry highly values the discovery of formulaic alphas for their interpretability and ease of analysis, compared with the expressive yet overfitting-prone black-box alphas. In th
Externí odkaz:
http://arxiv.org/abs/2406.16505
Autor:
Lin, Nankai, Wu, Hongyan, Chen, Zhengming, Li, Zijian, Wang, Lianxi, Jiang, Shengyi, Zhou, Dong, Yang, Aimin
Hate speech on social media is ubiquitous but urgently controlled. Without detecting and mitigating the biases brought by hate speech, different types of ethical problems. While a number of datasets have been proposed to address the problem of hate s
Externí odkaz:
http://arxiv.org/abs/2406.04876
Grammatical error correction (GEC) is a challenging task of natural language processing techniques. While more attempts are being made in this approach for universal languages like English or Chinese, relatively little work has been done for low-reso
Externí odkaz:
http://arxiv.org/abs/2303.17367
Autor:
Liu, Xu-Hui, Xu, Feng, Zhang, Xinyu, Liu, Tianyuan, Jiang, Shengyi, Chen, Ruifeng, Zhang, Zongzhang, Yu, Yang
Imitation learning aims to mimic the behavior of experts without explicit reward signals. Passive imitation learning methods which use static expert datasets typically suffer from compounding error, low sample efficiency, and high hyper-parameter sen
Externí odkaz:
http://arxiv.org/abs/2303.02073
Deep learning-based text classification models need abundant labeled data to obtain competitive performance. Unfortunately, annotating large-size corpus is time-consuming and laborious. To tackle this, multiple researches try to use data augmentation
Externí odkaz:
http://arxiv.org/abs/2302.00894
Chinese spelling check is a task to detect and correct spelling mistakes in Chinese text. Existing research aims to enhance the text representation and use multi-source information to improve the detection and correction capabilities of models, but d
Externí odkaz:
http://arxiv.org/abs/2210.13823
Chinese features prominently in the Chinese communities located in the nations of Malay Archipelago. In these countries, Chinese has undergone the process of adjustment to the local languages and cultures, which leads to the occurrence of a Chinese v
Externí odkaz:
http://arxiv.org/abs/2209.04611
Small subgraphs (graphlets) are important features to describe fundamental units of a large network. The calculation of the subgraph frequency distributions has a wide application in multiple domains including biology and engineering. Unfortunately d
Externí odkaz:
http://arxiv.org/abs/2207.06684