Hangbo Bao


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Attention Temperature Matters in Abstractive Summarization Distillation
Shengqiang Zhang | Xingxing Zhang | Hangbo Bao | Furu Wei
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Recent progress of abstractive text summarization largely relies on large pre-trained sequence-to-sequence Transformer models, which are computationally expensive. This paper aims to distill these large models into smaller ones for faster inference and with minimal performance loss. Pseudo-labeling based methods are popular in sequence-to-sequence model distillation. In this paper, we find simply manipulating attention temperatures in Transformers can make pseudo labels easier to learn for student models. Our experiments on three summarization datasets show our proposed method consistently improves vanilla pseudo-labeling based methods. Further empirical analysis shows that both pseudo labels and summaries produced by our students are shorter and more abstractive.

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THE-X: Privacy-Preserving Transformer Inference with Homomorphic Encryption
Tianyu Chen | Hangbo Bao | Shaohan Huang | Li Dong | Binxing Jiao | Daxin Jiang | Haoyi Zhou | Jianxin Li | Furu Wei
Findings of the Association for Computational Linguistics: ACL 2022

As more and more pre-trained language models adopt on-cloud deployment, the privacy issues grow quickly, mainly for the exposure of plain-text user data (e.g., search history, medical record, bank account). Privacy-preserving inference of transformer models is on the demand of cloud service users. To protect privacy, it is an attractive choice to compute only with ciphertext in homomorphic encryption (HE). However, enabling pre-trained models inference on ciphertext data is difficult due to the complex computations in transformer blocks, which are not supported by current HE tools yet. In this work, we introduce THE-X, an approximation approach for transformers, which enables privacy-preserving inference of pre-trained models developed by popular frameworks. THE-X proposes a workflow to deal with complex computation in transformer networks, including all the non-polynomial functions like GELU, softmax, and LayerNorm. Experiments reveal our proposed THE-X can enable transformer inference on encrypted data for different downstream tasks, all with negligible performance drop but enjoying the theory-guaranteed privacy-preserving advantage.


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MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers
Wenhui Wang | Hangbo Bao | Shaohan Huang | Li Dong | Furu Wei
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Learning to Sample Replacements for ELECTRA Pre-Training
Yaru Hao | Li Dong | Hangbo Bao | Ke Xu | Furu Wei
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


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Inspecting Unification of Encoding and Matching with Transformer: A Case Study of Machine Reading Comprehension
Hangbo Bao | Li Dong | Furu Wei | Wenhui Wang | Nan Yang | Lei Cui | Songhao Piao | Ming Zhou
Proceedings of the 2nd Workshop on Machine Reading for Question Answering

Most machine reading comprehension (MRC) models separately handle encoding and matching with different network architectures. In contrast, pretrained language models with Transformer layers, such as GPT (Radford et al., 2018) and BERT (Devlin et al., 2018), have achieved competitive performance on MRC. A research question that naturally arises is: apart from the benefits of pre-training, how many performance gain comes from the unified network architecture. In this work, we evaluate and analyze unifying encoding and matching components with Transformer for the MRC task. Experimental results on SQuAD show that the unified model outperforms previous networks that separately treat encoding and matching. We also introduce a metric to inspect whether a Transformer layer tends to perform encoding or matching. The analysis results show that the unified model learns different modeling strategies compared with previous manually-designed models.