Yi Wu


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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


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PILE: Pairwise Iterative Logits Ensemble for Multi-Teacher Labeled Distillation
Lianshang Cai | Linhao Zhang | Dehong Ma | Jun Fan | Daiting Shi | Yi Wu | Zhicong Cheng | Simiu Gu | Dawei Yin
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track

Pre-trained language models have become a crucial part of ranking systems and achieved very impressive effects recently. To maintain high performance while keeping efficient computations, knowledge distillation is widely used. In this paper, we focus on two key questions in knowledge distillation for ranking models: 1) how to ensemble knowledge from multi-teacher; 2) how to utilize the label information of data in the distillation process. We propose a unified algorithm called Pairwise Iterative Logits Ensemble (PILE) to tackle these two questions simultaneously. PILE ensembles multi-teacher logits supervised by label information in an iterative way and achieved competitive performance in both offline and online experiments. The proposed method has been deployed in a real-world commercial search system.


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BLCUFIGHT at SemEval-2021 Task 10: Novel Unsupervised Frameworks For Source-Free Domain Adaptation
Weikang Wang | Yi Wu | Yixiang Liu | Pengyuan Liu
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

Domain adaptation assumes that samples from source and target domains are freely accessible during a training phase. However, such assumption is rarely plausible in the real-world and may causes data-privacy issues, especially when the label of the source domain can be a sensitive attribute as an identifier. SemEval-2021 task 10 focuses on these issues. We participate in the task and propose novel frameworks based on self-training method. In our systems, two different frameworks are designed to solve text classification and sequence labeling. These approaches are tested to be effective which ranks the third among all system in subtask A, and ranks the first among all system in subtask B.


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Unsupervised Extractive Summarization by Pre-training Hierarchical Transformers
Shusheng Xu | Xingxing Zhang | Yi Wu | Furu Wei | Ming Zhou
Findings of the Association for Computational Linguistics: EMNLP 2020

Unsupervised extractive document summarization aims to select important sentences from a document without using labeled summaries during training. Existing methods are mostly graph-based with sentences as nodes and edge weights measured by sentence similarities. In this work, we find that transformer attentions can be used to rank sentences for unsupervised extractive summarization. Specifically, we first pre-train a hierarchical transformer model using unlabeled documents only. Then we propose a method to rank sentences using sentence-level self-attentions and pre-training objectives. Experiments on CNN/DailyMail and New York Times datasets show our model achieves state-of-the-art performance on unsupervised summarization. We also find in experiments that our model is less dependent on sentence positions. When using a linear combination of our model and a recent unsupervised model explicitly modeling sentence positions, we obtain even better results.


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Adversarial Training for Relation Extraction
Yi Wu | David Bamman | Stuart Russell
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Adversarial training is a mean of regularizing classification algorithms by generating adversarial noise to the training data. We apply adversarial training in relation extraction within the multi-instance multi-label learning framework. We evaluate various neural network architectures on two different datasets. Experimental results demonstrate that adversarial training is generally effective for both CNN and RNN models and significantly improves the precision of predicted relations.


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Improving the Annotation of Sentence Specificity
Junyi Jessy Li | Bridget O’Daniel | Yi Wu | Wenli Zhao | Ani Nenkova
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We introduce improved guidelines for annotation of sentence specificity, addressing the issues encountered in prior work. Our annotation provides judgements of sentences in context. Rather than binary judgements, we introduce a specificity scale which accommodates nuanced judgements. Our augmented annotation procedure also allows us to define where in the discourse context the lack of specificity can be resolved. In addition, the cause of the underspecification is annotated in the form of free text questions. We present results from a pilot annotation with this new scheme and demonstrate good inter-annotator agreement. We found that the lack of specificity distributes evenly among immediate prior context, long distance prior context and no prior context. We find that missing details that are not resolved in the the prior context are more likely to trigger questions about the reason behind events, “why” and “how”. Our data is accessible at http://www.cis.upenn.edu/~nlp/corpora/lrec16spec.html