Multi-Source Domain Adaptation for Text Classification via DistanceNet-Bandits (2001.04362v3)

Abstract: Domain adaptation performance of a learning algorithm on a target domain is a function of its source domain error and a divergence measure between the data distribution of these two domains. We present a study of various distance-based measures in the context of NLP tasks, that characterize the dissimilarity between domains based on sample estimates. We first conduct analysis experiments to show which of these distance measures can best differentiate samples from same versus different domains, and are correlated with empirical results. Next, we develop a DistanceNet model which uses these distance measures, or a mixture of these distance measures, as an additional loss function to be minimized jointly with the task's loss function, so as to achieve better unsupervised domain adaptation. Finally, we extend this model to a novel DistanceNet-Bandit model, which employs a multi-armed bandit controller to dynamically switch between multiple source domains and allow the model to learn an optimal trajectory and mixture of domains for transfer to the low-resource target domain. We conduct experiments on popular sentiment analysis datasets with several diverse domains and show that our DistanceNet model, as well as its dynamic bandit variant, can outperform competitive baselines in the context of unsupervised domain adaptation.

• The paper presents a novel multi-source domain adaptation approach that aligns feature distributions using advanced distance measures and a dynamic bandit controller.
• It integrates multiple distance metrics, such as MMD and Cosine, as additional loss functions to minimize domain discrepancies and improve classification outcomes.
• The dynamic multi-armed bandit method outperforms static baselines, offering a robust strategy for handling low-resource target domains in NLP.

Multi-Source Domain Adaptation for Text Classification via DistanceNet-Bandits: A Technical Overview

The paper "Multi-Source Domain Adaptation for Text Classification via DistanceNet-Bandits," authored by Han Guo, Ramakanth Pasunuru, and Mohit Bansal, presents a sophisticated approach to improving unsupervised domain adaptation in NLP tasks. This research addresses the critical challenge in domain adaptation, where a model trained on source domain data must be effectively adapted to a target domain with different data distributions, often without labeled examples from the target domain.

The paper introduces two primary innovations: the DistanceNet model and the DistanceNet-Bandit model. Both models focus on optimizing the performance on a low-resource target domain by minimizing the dissimilarity between source and target data distributions.

Distance Measures and DistanceNet Model

The DistanceNet model leverages several domain distance measures such as $\mathcal{L}_2$, Maximum Mean Discrepancy (MMD), Fisher Linear Discriminant (FDA), Cosine, and Correlation Alignment (CORAL). The distinctive feature of DistanceNet is the incorporation of these distance measures as additional loss functions, which are optimized alongside the primary task loss. This integration facilitates the alignment of feature distributions from the source to the target domain, aiming to develop domain-invariant features.

The evaluation centered around NLP tasks like sentiment analysis, where the efficacy of different distance measures was investigated according to their ability to differentiate between in-domain and out-of-domain samples. Among these, $\mathcal{L}_2$, MMD, and Cosine measures exhibited strong correlations with empirical model performance across domains.

DistanceNet-Bandit Model

The subsequent extension, the DistanceNet-Bandit model, introduces a dynamic element to multi-source domain training. By implementing a multi-armed bandit controller, this model dynamically selects from multiple source domains, optimizing the learning trajectory for the target domain. This is particularly crucial in scenarios with diverse source domains, where static combinations might not sufficiently capture the complexity of the target domain's data distribution.

Experimental Results

The experiments conducted on sentiment analysis datasets demonstrated the effectiveness of both the DistanceNet and DistanceNet-Bandit models. Notably, the multi-source dynamic selection approach outperformed static baselines, showcasing the potential of adaptive domain selection in improving domain adaptation performance. The favorability of the DistanceNet model with MMD and Cosine distance measures over traditional methods highlights the importance of domain distance minimization in unsupervised domain adaptation.

Implications and Future Directions

The practical implications of this paper are significant, especially for NLP tasks where acquiring labeled target data is challenging. The paper's approach provides a more robust method for handling domain shifts, essential for real-world applications across diverse fields like sentiment analysis, language translation, and more. Theoretically, the introduction of a dynamic, multi-source training approach presents an exciting avenue for further research into adaptive learning strategies, potentially leading to more generalizable AI models.

In the future, exploration into more sophisticated distance measures, perhaps driven by self-supervised or unsupervised learning advances, could further enhance cross-domain adaptation. Additionally, integrating these strategies with pre-trained LLMs may provide compounding benefits, given the pre-trained models' inherent capacity for leveraging linguistic features.

In conclusion, this paper contributes valuable insights into multi-source domain adaptation, presenting a viable strategy for achieving domain-invariant feature learning in unsupervised settings. The combination of distance measure analysis and dynamic domain selection offers a compelling paradigm for evolving AI applications in variable data environments.