Universal Domain Adaptation for Robust Handling of Distributional Shifts in NLP
This work addresses the need for robust NLP systems that can adapt to unfamiliar domains and detect anomalies, though it is incremental as it extends existing UniDA concepts from vision to language.
The authors tackled the problem of applying Universal Domain Adaptation (UniDA) to natural language processing by creating a comprehensive benchmark to evaluate model generalizability and robustness across distributional shifts, finding that existing UniDA methods from computer vision can be effectively transferred to NLP with performance influenced by adaptation difficulty.
When deploying machine learning systems to the wild, it is highly desirable for them to effectively leverage prior knowledge to the unfamiliar domain while also firing alarms to anomalous inputs. In order to address these requirements, Universal Domain Adaptation (UniDA) has emerged as a novel research area in computer vision, focusing on achieving both adaptation ability and robustness (i.e., the ability to detect out-of-distribution samples). While UniDA has led significant progress in computer vision, its application on language input still needs to be explored despite its feasibility. In this paper, we propose a comprehensive benchmark for natural language that offers thorough viewpoints of the model's generalizability and robustness. Our benchmark encompasses multiple datasets with varying difficulty levels and characteristics, including temporal shifts and diverse domains. On top of our testbed, we validate existing UniDA methods from computer vision and state-of-the-art domain adaptation techniques from NLP literature, yielding valuable findings: We observe that UniDA methods originally designed for image input can be effectively transferred to the natural language domain while also underscoring the effect of adaptation difficulty in determining the model's performance.