More Like this

1. It Takes Two Flints to Make a Fire: Multitask Learning of Neural Relation and Explanation Classifiers

   Tang, Zheng; Surdeanu, Mihai (April 2022, ArXivorg)

   We propose an explainable approach for relation extraction that mitigates the tension between generalization and explainability by jointly training for the two goals. Our approach uses a multi-task learning architecture, which jointly trains a classifier for relation extraction, and a sequence model that labels words in the context of the relation that explain the decisions of the relation classifier. We also convert the model outputs to rules to bring global explanations to this approach. This sequence model is trained using a hybrid strategy: supervised, when supervision from pre-existing patterns is available, and semi-supervised otherwise. In the latter situation, we treat the sequence model’s labels as latent variables, and learn the best assignment that maximizes the performance of the relation classifier. We evaluate the proposed approach on the two datasets and show that the sequence model provides labels that serve as accurate explanations for the relation classifier’s decisions, and, importantly, that the joint training generally improves the performance of the relation classifier. We also evaluate the performance of the generated rules and show that the new rules are great add-on to the manual rules and bring the rule-based system much closer to the neural models. 

   more » « less
2. Bootstrapping Neural Relation and Explanation Classifiers

   Zheng, Tang; Surdeanu, Mihai (July 2023, Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL))

   We introduce a method that self trains (or bootstraps) neural relation and explanation classifiers. Our work expands the supervised approach of (Tang and Surdeanu, 2022), which jointly trains a relation classifier with an explanation classifier that identifies context words important for the relation at hand, to semi- supervised scenarios. In particular, our approach iteratively converts the explainable mod- els’ outputs to rules and applies them to unlabeled text to produce new annotations. Our evaluation on the TACRED dataset shows that our method outperforms the rule-based model we started from by 15 F1 points, outperforms traditional self-training that relies just on the relation classifier by 5 F1 points, and performs comparatively with the prompt-based approach of Sainz et al. (2021) (without requiring an additional natural language inference component). 

   more » « less
3. Impact of Class Imbalance on Unsupervised Label Generation for Medicare Fraud Detection

   https://doi.org/10.1109/IRI62200.2024.00053  

   Robert_Kennedy, KL; Khoshgoftaar, Taghi M (August 2024, IEEE)

   Rubin, Stuart; Chen, Shu-Ching (Ed.)

   In this work, we use an unsupervised method for generating binary class labels in a novel context to create class labels for Medicare fraud detection. We examine how class imbalance influences the quality of these new labels and how it affects supervised classification. We use four different Medicare Part D fraud detection datasets, with the largest containing over 5 million instances. The other three datasets are sampled from the original dataset. Using Random Under-Sampling (RUS), we subsample from the majority class of the original data to produce three datasets with varying levels of class imbalance. To evaluate the performance of the newly created labels, we train a supervised classifier and evaluate its classification performance and compare it to an unsupervised anomaly detection method as a baseline. Our empirical findings indicate that the generated class labels are of high enough quality and enable effective supervised classifier training for fraud detection. Additionally, supervised classification with the new labels consistently outperforms the baseline used for comparison across all test scenarios. Further more, we observe an inverse relationship between class imbalance in the dataset and classifier performance, with AUPRC scores improving as the training dataset becomes more balanced. This work not only validates the efficacy of the synthesized class labels in labeling Medicare fraud but also shows its robustness across different degrees of class imbalance. 

   more » « less
4. PIEClass: Weakly-Supervised Text Classification with Prompting and Noise-Robust Iterative Ensemble Training

   https://doi.org/10.18653/v1/2023.emnlp-main.780  

   Zhang, Yunyi; Jiang, Minhao; Meng, Yu; Zhang, Yu; Han, Jiawei (January 2023, Association for Computational Linguistics)

   Weakly-supervised text classification trains a classifier using the label name of each target class as the only supervision, which largely reduces human annotation efforts. Most existing methods first use the label names as static keyword-based features to generate pseudo labels, which are then used for final classifier training. While reasonable, such a commonly adopted framework suffers from two limitations: (1) keywords can have different meanings in different contexts and some text may not have any keyword, so keyword matching can induce noisy and inadequate pseudo labels; (2) the errors made in the pseudo label generation stage will directly propagate to the classifier training stage without a chance of being corrected. In this paper, we propose a new method, PIEClass, consisting of two modules: (1) a pseudo label acquisition module that uses zero-shot prompting of pre-trained language models (PLM) to get pseudo labels based on contextualized text understanding beyond static keyword matching, and (2) a noise-robust iterative ensemble training module that iteratively trains classifiers and updates pseudo labels by utilizing two PLM fine-tuning methods that regularize each other. Extensive experiments show that PIEClass achieves overall better performance than existing strong baselines on seven benchmark datasets and even achieves similar performance to fully-supervised classifiers on sentiment classification tasks. 

   more » « less
5. Learning from Noisy Labels for Entity-Centric Information Extraction

   https://doi.org/10.18653/v1/2021.emnlp-main.437  

   Zhou, Wenxuan; Chen, Muhao (January 2021, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP))

   Recent information extraction approaches have relied on training deep neural models. However, such models can easily overfit noisy labels and suffer from performance degradation. While it is very costly to filter noisy labels in large learning resources, recent studies show that such labels take more training steps to be memorized and are more frequently forgotten than clean labels, therefore are identifiable in training. Motivated by such properties, we propose a simple co-regularization framework for entity-centric information extraction, which consists of several neural models with identical structures but different parameter initialization. These models are jointly optimized with the task-specific losses and are regularized to generate similar predictions based on an agreement loss, which prevents overfitting on noisy labels. Extensive experiments on two widely used but noisy benchmarks for information extraction, TACRED and CoNLL03, demonstrate the effectiveness of our framework. We release our code to the community for future research. 

   more » « less