1 code implementation • EMNLP 2020 • Xiaobao Wu, Chunping Li, Yan Zhu, Yishu Miao
Topic models have been prevailing for many years on discovering latent semantics while modeling long documents.
1 code implementation • 26 Mar 2024 • Cong-Duy Nguyen, Thong Nguyen, Xiaobao Wu, Anh Tuan Luu
Previous work on multimodal sentence embedding has proposed multimodal contrastive learning and achieved promising results.
no code implementations • 29 Feb 2024 • Xiaobao Wu, Liangming Pan, William Yang Wang, Anh Tuan Luu
In this paper, we propose a new benchmark, Unstructured Knowledge Editing (UKE).
no code implementations • 12 Feb 2024 • Thong Nguyen, Xiaobao Wu, Xinshuai Dong, Cong-Duy T Nguyen, See-Kiong Ng, Anh Tuan Luu
Secondly, we explicitly cast contrastive topic modeling as a gradient-based multi-objective optimization problem, with the goal of achieving a Pareto stationary solution that balances the trade-off between the ELBO and the contrastive objective.
2 code implementations • 27 Jan 2024 • Xiaobao Wu, Thong Nguyen, Anh Tuan Luu
In this paper, we present a comprehensive survey on neural topic models concerning methods, applications, and challenges.
1 code implementation • 25 Jan 2024 • Xiaobao Wu, Fengjun Pan, Thong Nguyen, Yichao Feng, Chaoqun Liu, Cong-Duy Nguyen, Anh Tuan Luu
Hierarchical topic modeling aims to discover latent topics from a corpus and organize them into a hierarchy to understand documents with desirable semantic granularity.
1 code implementation • 12 Dec 2023 • Thong Nguyen, Xiaobao Wu, Xinshuai Dong, Khoi Le, Zhiyuan Hu, Cong-Duy Nguyen, See-Kiong Ng, Luu Anh Tuan
Fully fine-tuning pretrained large-scale transformer models has become a popular paradigm for video-language modeling tasks, such as temporal language grounding and video-language summarization.
no code implementations • 5 Dec 2023 • Thong Nguyen, Xiaobao Wu, Xinshuai Dong, Cong-Duy Nguyen, See-Kiong Ng, Luu Anh Tuan
Temporal Language Grounding seeks to localize video moments that semantically correspond to a natural language query.
1 code implementation • 13 Sep 2023 • Xiaobao Wu, Fengjun Pan, Anh Tuan Luu
Topic models have been proposed for decades with various applications and recently refreshed by the neural variational inference.
1 code implementation • 7 Jun 2023 • Xiaobao Wu, Xinshuai Dong, Thong Nguyen, Anh Tuan Luu
Topic models have been prevalent for decades with various applications.
1 code implementation • 22 May 2023 • Thong Nguyen, Xiaobao Wu, Xinshuai Dong, Anh Tuan Luu, Cong-Duy Nguyen, Zhen Hai, Lidong Bing
Multimodal Review Helpfulness Prediction (MRHP) aims to rank product reviews based on predicted helpfulness scores and has been widely applied in e-commerce via presenting customers with useful reviews.
1 code implementation • 22 May 2023 • Liangming Pan, Xiaobao Wu, Xinyuan Lu, Anh Tuan Luu, William Yang Wang, Min-Yen Kan, Preslav Nakov
Fact-checking real-world claims often requires collecting multiple pieces of evidence and applying complex multi-step reasoning.
1 code implementation • 19 May 2023 • Chaoqun Liu, Wenxuan Zhang, Guizhen Chen, Xiaobao Wu, Anh Tuan Luu, Chip Hong Chang, Lidong Bing
In this work, we propose a new paradigm based on self-supervised learning to solve zero-shot text classification tasks by tuning the language models with unlabeled data, called self-supervised tuning.
1 code implementation • 7 Apr 2023 • Xiaobao Wu, Xinshuai Dong, Thong Nguyen, Chaoqun Liu, Liangming Pan, Anh Tuan Luu
Instead of the direct alignment in previous work, we propose a topic alignment with mutual information method.
1 code implementation • 23 Nov 2022 • Xiaobao Wu, Anh Tuan Luu, Xinshuai Dong
To overcome the data sparsity issue in short text topic modeling, existing methods commonly rely on data augmentation or the data characteristic of short texts to introduce more word co-occurrence information.
1 code implementation • 7 Nov 2022 • Thong Nguyen, Xiaobao Wu, Anh-Tuan Luu, Cong-Duy Nguyen, Zhen Hai, Lidong Bing
To overcome the aforementioned issues, we propose Multimodal Contrastive Learning for Multimodal Review Helpfulness Prediction (MRHP) problem, concentrating on mutual information between input modalities to explicitly elaborate cross-modal relations.
1 code implementation • 5 Jul 2022 • Thong Nguyen, Cong-Duy Nguyen, Xiaobao Wu, See-Kiong Ng, Anh Tuan Luu
Moreover, a list of training datasets and downstream tasks is supplied to further polish the perspective into V\&L pretraining.