Domain Classification-based Source-specific Term Penalization for Domain Adaptation in Hate-speech Detection
State-of-the-art approaches for hate-speech detection usually exhibit poor performance in out-of-domain settings. This occurs, typically, due to classifiers overemphasizing source-specific information that negatively impacts its domain invariance. Prior work has attempted to penalize terms related to hate-speech from manually curated lists using feature attribution methods, which quantify the importance assigned to input terms by the classifier when making a prediction. We, instead, propose a domain adaptation approach that automatically extracts and penalizes source-specific terms using a domain classifier, which learns to differentiate between domains, and feature-attribution scores for hate-speech classes, yielding consistent improvements in cross-domain evaluation.
Code (0)
등록된 구현이 없습니다.
Tasks
Domain Adaptationdomain classificationHate Speech DetectionSimilar Papers 제목 키워드 기반
Pseudo-Adaptive Penalization to Handle Constraints in Particle Swarm Optimizers
The penalization method is a popular technique to provide particle swarm optimizers with the ability to handle constraints. The downside is the need of penalization coefficients whose settings are problem-specific. While…
FixBi: Bridging Domain Spaces for Unsupervised Domain Adaptation
Unsupervised domain adaptation (UDA) methods for learning domain invariant representations have achieved remarkable progress. However, most of the studies were based on direct adaptation from the source domain to the tar…
Domain AdaptationUnsupervised Domain AdaptationA deep learning approach to solve forward differential problems on graphs
We propose a novel deep learning (DL) approach to solve one-dimensional non-linear elliptic, parabolic, and hyperbolic problems on graphs. A system of physics-informed neural network (PINN) models is used to solve the di…
Source-free Domain Adaptation Requires Penalized Diversity
While neural networks are capable of achieving human-like performance in many tasks such as image classification, the impressive performance of each model is limited to its own dataset. Source-free domain adaptation (SFD…
DiversityDomain Adaptationimage-classificationImage Classification+2Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation
Adversarial domain adaptation has made remarkable advances in learning transferable representations for knowledge transfer across domains. While adversarial learning strengthens the feature transferability which the comm…
Domain AdaptationTransfer Learning