Publication details
On Eliminating Inductive Biases of Deep Language Models
| Authors | |
|---|---|
| Year of publication | 2021 |
| Type | Appeared in Conference without Proceedings |
| MU Faculty or unit | |
| Citation | |
| Description | This poster outlines problems of modern neural language models with out-of-domain performance. It suggests that this might be a consequence of narrow model specialization. In order to eliminate this flaw, it suggests two main directions of future work: 1. Introduction of evaluative metrics can identify out-of-domain generalization abilities, while 2. Objective approach adjusts the training objective to respect the desired generalization properties of the system. |
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