AdaptOr: Objective-Centric Adaptation Framework for Language Models
| Authors | |
|---|---|
| Year of publication | 2022 |
| Type | Article in Proceedings |
| Conference | Proceedings of the 60th Conference of Association of Computational Linguistics, ACL 2022 |
| MU Faculty or unit | |
| Citation | |
| web | |
| Doi | http://dx.doi.org/10.18653/v1/2022.acl-demo.26 |
| Keywords | Adaptor library; domain adaptation; similarity search; vector space; embeddings |
| Description | Progress in natural language processing research is catalyzed by the possibilities given by the widespread software frameworks. This paper introduces the Adaptor library that transposes the traditional model-centric approach composed of pre-training + fine-tuning steps to the objective-centric approach, composing the training process by applications of selected objectives. We survey research directions that can benefit from enhanced objective-centric experimentation in multitask training, custom objectives development, dynamic training curricula, or domain adaptation. Adaptor aims to ease the reproducibility of these research directions in practice. Finally, we demonstrate the practical applicability of Adaptor in selected unsupervised domain adaptation scenarios. |
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