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1. Uncertainty-aware Self-supervised 3D Data Association

   Wang, Jianren; Ancha, Siddharth; Chen, Yi-Ting; Held, David (January 2020, Proceedings of the IEEERSJ International Conference on Intelligent Robots and Systems)

   null (Ed.)

   3D object trackers usually require training on large amounts of annotated data that is expensive and time-consuming to collect. Instead, we propose leveraging vast unlabeled datasets by self-supervised metric learning of 3D object trackers, with a focus on data association. Large scale annotations for unlabeled data are cheaply obtained by automatic object detection and association across frames. We show how these self-supervised annotations can be used in a principled manner to learn point-cloud embeddings that are effective for 3D tracking. We estimate and incorporate uncertainty in self-supervised tracking to learn more robust embeddings, without needing any labeled data. We design embeddings to differentiate objects across frames, and learn them using uncertainty-aware self-supervised training. Finally, we demonstrate their ability to perform accurate data association across frames, towards effective and accurate 3D tracking. 

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2. Document-Level Event Argument Extraction by Conditional Generation

   https://doi.org/10.18653/v1/2021.naacl-main.69  

   Li, Sha; Ji, Heng; Han, Jiawei (June 2021, Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, {NAACL-HLT} 2021)

   null (Ed.)

   Event extraction has long been treated as a sentence-level task in the IE community. We argue that this setting does not match human information seeking behavior and leads to incomplete and uninformative extraction results. We propose a document-level neural event argument extraction model by formulating the task as conditional generation following event templates. We also compile a new document-level event extraction benchmark dataset WIKIEVENTS which includes complete event and coreference annotation. On the task of argument extraction, we achieve an absolute gain of 7.6% F1 and 5.7% F1 over the next best model on the RAMS and WIKIEVENTS datasets respectively. On the more challenging task of informative argument extraction, which requires implicit coreference reasoning, we achieve a 9.3% F1 gain over the best baseline. To demonstrate the portability of our model, we also create the first end-to-end zero-shot event extraction framework and achieve 97% of fully supervised model’s trigger extraction performance and 82% of the argument extraction performance given only access to 10 out of the 33 types on ACE. 

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3. Personalized Speech Enhancement Through Self-Supervised Data Augmentation and Purification

   https://doi.org/10.21437/Interspeech.2021-1868  

   Sivaraman, Aswin; Kim, Sunwoo; Kim, Minje (August 2021, Proceedings of the Interspeech 2021)

   Training personalized speech enhancement models is innately a no-shot learning problem due to privacy constraints and limited access to noise-free speech from the target user. If there is an abundance of unlabeled noisy speech from the test-time user, one may train a personalized speech enhancement model using self-supervised learning. One straightforward approach to model personalization is to use the target speaker’s noisy recordings as pseudo-sources. Then, a pseudo denoising model learns to remove injected training noises and recover the pseudo-sources. However, this approach is volatile as it depends on the quality of the pseudo-sources, which may be too noisy. To remedy this, we propose a data purification step that refines the self-supervised approach. We first train an SNR predictor model to estimate the frame-by-frame SNR of the pseudo- sources. Then, we convert the predictor’s estimates into weights that adjust the pseudo-sources’ frame-by-frame contribution to- wards training the personalized model. We empirically show that the proposed data purification step improves the usability of the speaker-specific noisy data in the context of personalized speech enhancement. Our approach may be seen as privacy-preserving as it does not rely on any clean speech recordings or speaker embeddings. 

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4. Defending Graph Convolutional Networks against Dynamic Graph Perturbations via Bayesian Self-supervision,

   https://doi.org/10.1609/aaai.v36i4.20362  

   Zhuang, Jun; Hasan, Mohammad A (January 2022, Proceedings of the AAAI Conference on Artificial Intelligence)

   In recent years, plentiful evidence illustrates that Graph Con- volutional Networks (GCNs) achieve extraordinary accom- plishments on the node classification task. However, GCNs may be vulnerable to adversarial attacks on label-scarce dy- namic graphs. Many existing works aim to strengthen the ro- bustness of GCNs; for instance, adversarial training is used to shield GCNs against malicious perturbations. However, these works fail on dynamic graphs for which label scarcity is a pressing issue. To overcome label scarcity, self-training attempts to iteratively assign pseudo-labels to highly confi- dent unlabeled nodes but such attempts may suffer serious degradation under dynamic graph perturbations. In this paper, we generalize noisy supervision as a kind of self-supervised learning method and then propose a novel Bayesian self- supervision model, namely GraphSS, to address the issue. Extensive experiments demonstrate that GraphSS can not only affirmatively alert the perturbations on dynamic graphs but also effectively recover the prediction of a node classifier when the graph is under such perturbations. These two advan- tages prove to be generalized over three classic GCNs across five public graph datasets. 

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5. Weakly-Supervised Neural Text Classification

   https://doi.org/10.1145/3269206.3271737  

   Meng, Yu; Shen, Jiaming; Zhang, Chao; Han, Jiawei (October 2018, Proceedings of the 27th {ACM} International Conference on Information and Knowledge Management, {CIKM} 2018)

   Deep neural networks are gaining increasing popularity for the classic text classification task, due to their strong expressive power and less requirement for feature engineering. Despite such attractiveness, neural text classification models suffer from the lack of training data in many real-world applications. Although many semisupervised and weakly-supervised text classification models exist, they cannot be easily applied to deep neural models and meanwhile support limited supervision types. In this paper, we propose a weakly-supervised method that addresses the lack of training data in neural text classification. Our method consists of two modules: (1) a pseudo-document generator that leverages seed information to generate pseudo-labeled documents for model pre-training, and (2) a self-training module that bootstraps on real unlabeled data for model refinement. Our method has the flexibility to handle different types of weak supervision and can be easily integrated into existing deep neural models for text classification. We have performed extensive experiments on three real-world datasets from different domains. The results demonstrate that our proposed method achieves inspiring performance without requiring excessive training data and outperforms baseline methods significantly. 

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