Divide and Denoise: Learning from Noisy Labels in Fine-Grained Entity Typing with Cluster-Wise Loss Correction (2022)

Abstract: Fine-grained Entity Typing (FET) has made great progress based on distant supervision but still suffers from label noise. Existing FET noise learning methods rely on prediction distributions in an instance-independent manner, which causes the problem of confirmation bias. In this work, we propose a clustering-based loss correction framework named Feature Cluster Loss Correction (FCLC), to address these two problems. FCLC first train a coarse backbone model as a feature extractor and noise estimator. Loss correction is then applied to each feature cluster, learning directly from the noisy labels. Experimental results on three public datasets show that FCLC achieves the best performance over existing competitive systems. Auxiliary experiments further demonstrate that FCLC is stable to hyperparameters and it does help mitigate confirmation bias. We also find that in the extreme case of no clean data, the FCLC framework still achieves competitive performance.

## introduction fine-grained entity typing (fet) is the task of classifying named entity mentions in a sentence over the given class set (typically a hierarchical class structure as shown in fig. 1 . fet serves as an important component in many down-stream nlp applications, e.g., relation extraction @xcite , entity linking @xcite and question answering @xcite . fet task has a more wide range of entity types (usually over 100 classes) compared to entity typing, and hence neural-based fet systems require largescale annotated training corpus. recent studies apply distant supervision to label the corpora automatically by linking mentions to knowledge base entities and using all entity types † these authors contributed equally to this work and should be considered co-first authors. these two limitations are inter-related, causing noise-learning-based fet methods to still suffer from distantly supervised noise. to alleviate the label noise and avert these limitations, we propose a novel framework fclc for noisy label learning inspired by weighted training and loss correction @xcite in machine learning. our method utilizes feature representations from the model and learns global (local) information, i.e. a cluster-level label confusion matrix. firstly, we use a backbone learner on noisy data. it serves as a feature extractor and a noise estimator. secondly, all training data, including noisy data and a small portion of clean data are clustered. the clean data serve as anchors in the feature space to estimate label corruption and sample quality of each cluster. finally, label corruption and sample quality are used for label correction. our main contributions are three-fold: (i) this study provides fresh insight into instance dependent label noise in fet. we pointed out a novel training method to further exploit feature space and global information. (ii) we designed a framework with feature clustering, estimating cluster-level confusion matrix, and loss correction. (iii) we experimented the proposed method on three datasets. results show that we made significant improvements over previous state-of-the-art, thus proving the effectiveness of our model. ablation studies further prove the robustness and wide applicability of our framework. ## experiments we evaluate the proposed model on three different fet datasets and compare it to several state-ofthe-art models. in addition, to support our claims we also conduct several subsidiary experiments to analyze the impacts of our proposed module in detail. ## conclusion in this work, in order to tackle the instancedependent label noise in fine-grained entity typing tasks, we present a neural fet noisy learning framework that utilizes the feature space information and global information jointly. experimental results on three publicly available datasets demonstrate that our proposed model achieves the best performance compared with competitive existing fet systems. furthermore, based on extensive auxiliary experiments, we study the impact of our proposed noisy learning framework in-depth with qualitative and quantitative analysis. in the future, the proposed approach can motivate the need for further understanding of the relationships between dataset noise distribution estimation and the instance features. more work can be done towards this direction. in addition, performances of the proposed framework under different backbone models can be dug to validate the flexibility of the framework.