Abstract
Language models are defined over a finite set of inputs, which creates a vocabulary bottleneck when we attempt to scale the number of supported languages. Tackling this bottleneck results in a trade-off between what can be represented in the embedding matrix and computational issues in the output layer. This paper introduces PIXEL, the Pixel-based Encoder of Language, which suffers from neither of these issues. PIXEL is a pretrained language model that renders text as images, making it possible to transfer representations across languages based on orthographic similarity or the co-activation of pixels. PIXEL is trained to reconstruct the pixels of masked patches instead of predicting a distribution over tokens. We pretrain the 86M parameter PIXEL model on the same English data as BERT and evaluate on syntactic and semantic tasks in typologically diverse languages, including various non-Latin scripts. We find that PIXEL substantially outperforms BERT on syntactic and semantic processing tasks on scripts that are not found in the pretraining data, but PIXEL is slightly weaker than BERT when working with Latin scripts. Furthermore, we find that PIXEL is more robust than BERT to orthographic attacks and linguistic code-switching, further confirming the benefits of modelling language with pixels.
Originalsprog | Engelsk |
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Titel | The Eleventh International Conference on Learning Representations |
Antal sider | 32 |
Forlag | arXiv.org |
Publikationsdato | 2023 |
Status | Udgivet - 2023 |
Begivenhed | 11h International Conference on Learning Representations - ICLR 2023 - Kigali, Rwanda Varighed: 1 maj 2023 → 5 maj 2023 |
Konference
Konference | 11h International Conference on Learning Representations - ICLR 2023 |
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Land/Område | Rwanda |
By | Kigali |
Periode | 01/05/2023 → 05/05/2023 |