Knowledge Commons of Institute of Automation,CAS
Generating Relevant Article Comments via Variational Multi-Layer Fusion | |
Zou HY(邹瀚仪)1,2![]() ![]() ![]() ![]() | |
2024-03 | |
会议名称 | The 2024 International Joint Conference on Neural Networks |
会议日期 | 2024-7 |
会议地点 | Yokohama, Japan |
摘要 | Article comment generation is a novel and challenging task in natural language generation, which has attracted widespread attention from researchers in recent years. High-quality article comments such as relevant, diverse, and informative ones can greatly promote user interactions and enhance the user experience. However, current research works generally overlook the relevance between comments and the source article, which may generate mediocre and dull comments. To address this problem, a variational multi-layer fusion model (VMFM) based on variational auto-encoder (VAE) is proposed in this paper. The posterior distribution of the proposed VMFM is employed to supervise the prior network in selecting context-related latent variables from the source article, which are further integrated into the decoder to increase the relevance between generated comments and the source article. Due to the sequential nature of text generation, the influence of those latent variables on the decoder gradually diminishes during auto-regressive decoding. To mitigate this issue, we propose a multi-layer fusion method, which fuses a series of context-related latent variables extracted from the source article into every decoder layer. Experiments on four datasets show that our model significantly outperforms strong baselines in relevance, diversity, informativeness and fluency of generated comments based on automatic and human evaluations. |
关键词 | article comment generation variational auto-encoder relevant information extraction multi-layer fusion |
学科门类 | 工学::计算机科学与技术(可授工学、理学学位) |
收录类别 | EI |
语种 | 英语 |
是否为代表性论文 | 是 |
七大方向——子方向分类 | 自然语言处理 |
国重实验室规划方向分类 | 语音语言处理 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/57542 |
专题 | 多模态人工智能系统全国重点实验室_互联网大数据与信息安全 |
通讯作者 | Kong QC(孔庆超) |
作者单位 | 1.中国科学院自动化研究所 2.中国科学院大学人工智能学院 3.中国电力科学研究院 |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Zou HY,Xu HF,Kong QC,et al. Generating Relevant Article Comments via Variational Multi-Layer Fusion[C],2024. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
邹瀚仪_IJCNN2024_paper_(354KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论