Bin Luo


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ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding
Qiming Peng | Yinxu Pan | Wenjin Wang | Bin Luo | Zhenyu Zhang | Zhengjie Huang | Yuhui Cao | Weichong Yin | Yongfeng Chen | Yin Zhang | Shikun Feng | Yu Sun | Hao Tian | Hua Wu | Haifeng Wang
Findings of the Association for Computational Linguistics: EMNLP 2022

Recent years have witnessed the rise and success of pre-training techniques in visually-rich document understanding. However, most existing methods lack the systematic mining and utilization of layout-centered knowledge, leading to sub-optimal performances. In this paper, we propose ERNIE-Layout, a novel document pre-training solution with layout knowledge enhancement in the whole workflow, to learn better representations that combine the features from text, layout, and image. Specifically, we first rearrange input sequences in the serialization stage, and then present a correlative pre-training task, reading order prediction, to learn the proper reading order of documents. To improve the layout awareness of the model, we integrate a spatial-aware disentangled attention into the multi-modal transformer and a replaced regions prediction task into the pre-training phase. Experimental results show that ERNIE-Layout achieves superior performance on various downstream tasks, setting new state-of-the-art on key information extraction, document image classification, and document question answering datasets. The code and models are publicly available at PaddleNLP.


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Don’t Miss the Potential Customers! Retrieving Similar Ads to Improve User Targeting
Yi Feng | Ting Wang | Chuanyi Li | Vincent Ng | Jidong Ge | Bin Luo | Yucheng Hu | Xiaopeng Zhang
Findings of the Association for Computational Linguistics: EMNLP 2021

User targeting is an essential task in the modern advertising industry: given a package of ads for a particular category of products (e.g., green tea), identify the online users to whom the ad package should be targeted. A (ad package specific) user targeting model is typically trained using historical clickthrough data: positive instances correspond to users who have clicked on an ad in the package before, whereas negative instances correspond to users who have not clicked on any ads in the package that were displayed to them. Collecting a sufficient amount of positive training data for training an accurate user targeting model, however, is by no means trivial. This paper focuses on the development of a method for automatic augmentation of the set of positive training instances. Experimental results on two datasets, including a real-world company dataset, demonstrate the effectiveness of our proposed method.


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Identifying Exaggerated Language
Li Kong | Chuanyi Li | Jidong Ge | Bin Luo | Vincent Ng
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

While hyperbole is one of the most prevalent rhetorical devices, it is arguably one of the least studied devices in the figurative language processing community. We contribute to the study of hyperbole by (1) creating a corpus focusing on sentence-level hyperbole detection, (2) performing a statistical and manual analysis of our corpus, and (3) addressing the automatic hyperbole detection task.