Hui Li


2024

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MMAPS: End-to-End Multi-Grained Multi-Modal Attribute-Aware Product Summarization
Tao Chen | Ze Lin | Hui Li | Jiayi Ji | Yiyi Zhou | Guanbin Li | Rongrong Ji
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Given the long textual product information and the product image, Multi-modal Product Summarization (MPS) aims to increase customers’ desire to purchase by highlighting product characteristics with a short textual summary. Existing MPS methods can produce promising results. Nevertheless, they still 1) lack end-to-end product summarization, 2) lack multi-grained multi-modal modeling, and 3) lack multi-modal attribute modeling. To improve MPS, we propose an end-to-end multi-grained multi-modal attribute-aware product summarization method (MMAPS) for generating high-quality product summaries in e-commerce. MMAPS jointly models product attributes and generates product summaries. We design several multi-grained multi-modal tasks to better guide the multi-modal learning of MMAPS. Furthermore, we model product attributes based on both text and image modalities so that multi-modal product characteristics can be manifested in the generated summaries. Extensive experiments on a real large-scale Chinese e-commence dataset demonstrate that our model outperforms state-of-the-art product summarization methods w.r.t. several summarization metrics. Our code is publicly available at: https://github.com/KDEGroup/MMAPS.

2023

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DeRisk: An Effective Deep Learning Framework for Credit Risk Prediction over Real-World Financial Data
Yancheng Liang | Jiajie Zhang | Hui Li | Xiaochen Liu | Yi Hu | Yong Wu | Jiaoyao Zhang | Yongyan Liu | Yi Wu
Proceedings of the Fifth Workshop on Financial Technology and Natural Language Processing and the Second Multimodal AI For Financial Forecasting

2018

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LRMM: Learning to Recommend with Missing Modalities
Cheng Wang | Mathias Niepert | Hui Li
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Multimodal learning has shown promising performance in content-based recommendation due to the auxiliary user and item information of multiple modalities such as text and images. However, the problem of incomplete and missing modality is rarely explored and most existing methods fail in learning a recommendation model with missing or corrupted modalities. In this paper, we propose LRMM, a novel framework that mitigates not only the problem of missing modalities but also more generally the cold-start problem of recommender systems. We propose modality dropout (m-drop) and a multimodal sequential autoencoder (m-auto) to learn multimodal representations for complementing and imputing missing modalities. Extensive experiments on real-world Amazon data show that LRMM achieves state-of-the-art performance on rating prediction tasks. More importantly, LRMM is more robust to previous methods in alleviating data-sparsity and the cold-start problem.

2014

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Chinese Temporal Tagging with HeidelTime
Hui Li | Jannik Strötgen | Julian Zell | Michael Gertz
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, volume 2: Short Papers

1997

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Incorporating Bigram Constraints into an LR Table
Hiroki Imai | Hui Li | Hozumi Tanaka
Proceedings of the 10th Research on Computational Linguistics International Conference