Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Xu, Zenan"'
Autor:
Chen, Jiachi, Zhong, Qingyuan, Wang, Yanlin, Ning, Kaiwen, Liu, Yongkun, Xu, Zenan, Zhao, Zhe, Chen, Ting, Zheng, Zibin
The emergence of Large Language Models (LLMs) has significantly influenced various aspects of software development activities. Despite their benefits, LLMs also pose notable risks, including the potential to generate harmful content and being abused
Externí odkaz:
http://arxiv.org/abs/2409.15154
Multimodal abstractive summarization for videos (MAS) requires generating a concise textual summary to describe the highlights of a video according to multimodal resources, in our case, the video content and its transcript. Inspired by the success of
Externí odkaz:
http://arxiv.org/abs/2305.04824
A key challenge in video question answering is how to realize the cross-modal semantic alignment between textual concepts and corresponding visual objects. Existing methods mostly seek to align the word representations with the video regions. However
Externí odkaz:
http://arxiv.org/abs/2205.06530
Publikováno v:
In Journal of Hydrology April 2024 633
Autor:
Zhong, Wanjun, Wang, Siyuan, Tang, Duyu, Xu, Zenan, Guo, Daya, Wang, Jiahai, Yin, Jian, Zhou, Ming, Duan, Nan
Analytical reasoning is an essential and challenging task that requires a system to analyze a scenario involving a set of particular circumstances and perform reasoning over it to make conclusions. In this paper, we study the challenge of analytical
Externí odkaz:
http://arxiv.org/abs/2104.06598
Autor:
Xu, Zenan, Guo, Daya, Tang, Duyu, Su, Qinliang, Shou, Linjun, Gong, Ming, Zhong, Wanjun, Quan, Xiaojun, Duan, Nan, Jiang, Daxin
We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa. Existing methods utilize syntax of text either in the pre-training stage or in the fine-tuning stage, so that they suffer from
Externí odkaz:
http://arxiv.org/abs/2012.14116
Network embedding has recently emerged as a promising technique to embed nodes of a network into low-dimensional vectors. While fairly successful, most existing works focus on the embedding techniques for static networks. But in practice, there are m
Externí odkaz:
http://arxiv.org/abs/2010.14047
Autor:
Zhong, Wanjun, Tang, Duyu, Xu, Zenan, Wang, Ruize, Duan, Nan, Zhou, Ming, Wang, Jiahai, Yin, Jian
Deepfake detection, the task of automatically discriminating machine-generated text, is increasingly critical with recent advances in natural language generative models. Existing approaches to deepfake detection typically represent documents with coa
Externí odkaz:
http://arxiv.org/abs/2010.07475
Autor:
Zhong, Wanjun, Xu, Jingjing, Tang, Duyu, Xu, Zenan, Duan, Nan, Zhou, Ming, Wang, Jiahai, Yin, Jian
Fact checking is a challenging task because verifying the truthfulness of a claim requires reasoning about multiple retrievable evidence. In this work, we present a method suitable for reasoning about the semantic-level structure of evidence. Unlike
Externí odkaz:
http://arxiv.org/abs/1909.03745
Textual network embeddings aim to learn a low-dimensional representation for every node in the network so that both the structural and textual information from the networks can be well preserved in the representations. Traditionally, the structural a
Externí odkaz:
http://arxiv.org/abs/1908.11057