Towards Domain Robustness of Neural Language Models
| Authors | |
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| Year of publication | 2021 |
| Type | Article in Proceedings |
| Conference | Recent Advances in Slavonic Natural Language Processing (RASLAN 2021) |
| MU Faculty or unit | |
| Citation | |
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| Keywords | Generalization; Debiasing; Domain extrapolation; Domain adaptation; Domain robustness; Neural language models |
| Description | This work summarises recent progress in generalization evaluation and training of deep neural networks, categorized in data-centric and model-centric overviews. Grounded in the results of the referenced work, we propose three future directions towards reaching higher robustness of language models to an unknown domain or its adaptation to an existing domain of interest. In the example propositions that practically complement each of the directions, we introduce novel ideas of a) dynamic objective selection, b) language modeling respecting the token similarities to the ground truth and c) a framework of additive component of the loss utilizing the well-performing generalization measures. |
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