Abstract
Recent work attributes progress in NLP to large language models (LMs) with increased model size and large quantities of pretraining data. Despite this, current state-of-the-art LMs for Hebrew are both under-parameterized and under-trained compared to LMs in other languages. Additionally, previous work on pretrained Hebrew LMs focused on encoder-only models. While the encoder-only architecture is beneficial for classification tasks, it does not cater well for sub-word prediction tasks, such as Named Entity Recognition, when considering the morphologically rich nature of Hebrew. In this paper we argue that sequence-to-sequence generative architectures are more suitable for large LMs in morphologically rich languages (MRLs) such as Hebrew. We demonstrate this by casting tasks in the Hebrew NLP pipeline as text-to-text tasks, for which we can leverage powerful multilingual, pretrained sequence-to-sequence models as mT5, eliminating the need for a separate, specialized, morpheme-based, decoder. Using this approach, our experiments show substantial improvements over previously published results on all existing Hebrew NLP benchmarks. These results suggest that multilingual sequence-to-sequence models present a promising building block for NLP for MRLs.
Original language | English |
---|---|
Title of host publication | Findings of the Association for Computational Linguistics, ACL 2023 |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 7700-7708 |
Number of pages | 9 |
ISBN (Electronic) | 9781959429623 |
State | Published - 2023 |
Externally published | Yes |
Event | 61st Annual Meeting of the Association for Computational Linguistics, ACL 2023 - Toronto, Canada Duration: 9 Jul 2023 → 14 Jul 2023 |
Publication series
Name | Proceedings of the Annual Meeting of the Association for Computational Linguistics |
---|---|
ISSN (Print) | 0736-587X |
Conference
Conference | 61st Annual Meeting of the Association for Computational Linguistics, ACL 2023 |
---|---|
Country/Territory | Canada |
City | Toronto |
Period | 9/07/23 → 14/07/23 |
Bibliographical note
Publisher Copyright:© 2023 Association for Computational Linguistics.