UDApter -- Efficient Domain Adaptation Using Adapters

• URL: http://arxiv.org/abs/2302.03194v1
• Date: Tue, 7 Feb 2023 02:04:17 GMT
• Title: UDApter -- Efficient Domain Adaptation Using Adapters
• Authors: Bhavitvya Malik, Abhinav Ramesh Kashyap, Min-Yen Kan, Soujanya Poria
• Abstract summary: We propose two methods to make unsupervised domain adaptation more parameter efficient. The first method deconstructs UDA into a two-step process: first by adding a domain adapter to learn domain-invariant information. We are within 0.85% F1 for natural language inference task, by fine-tuning only a fraction of the full model parameters.
• Score: 29.70751969196527
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose two methods to make unsupervised domain adaptation (UDA) more parameter efficient using adapters, small bottleneck layers interspersed with every layer of the large-scale pre-trained language model (PLM). The first method deconstructs UDA into a two-step process: first by adding a domain adapter to learn domain-invariant information and then by adding a task adapter that uses domain-invariant information to learn task representations in the source domain. The second method jointly learns a supervised classifier while reducing the divergence measure. Compared to strong baselines, our simple methods perform well in natural language inference (MNLI) and the cross-domain sentiment classification task. We even outperform unsupervised domain adaptation methods such as DANN and DSN in sentiment classification, and we are within 0.85% F1 for natural language inference task, by fine-tuning only a fraction of the full model parameters. We release our code at https://github.com/declare-lab/UDAPTER

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