Efficient Hierarchical Domain Adaptation for Pretrained Language Models
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Generative language models are trained on diverse, general domain corpora. However, this limits their applicability to narrower domains, and prior work has shown that continued in-domain training can provide further gains. In this paper, we introduce a method to scale domain adaptation to many diverse domains using a computationally efficient adapter approach. Our method is based on the observation that textual domains are partially overlapping, and we represent domains as a hierarchical tree structure where each node in the tree is associated with a set of adapter weights. When combined with a frozen pretrained language model, this approach enables parameter sharing among related domains, while avoiding negative interference between unrelated ones. It is efficient and computational cost scales as O(log(D)) for D domains. Experimental results with GPT-2 and a large fraction of the 100 most represented websites in C4 show across-the-board improvements in-domain. We additionally provide an inference time algorithm for a held-out domain and show that averaging over multiple paths through the tree enables further gains in generalization, while adding only a marginal cost to inference.
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