Language Model Pre-Training with Sparse Latent Typing
Liliang Ren, Zixuan Zhang, Han Wang, Clare Voss, ChengXiang Zhai, Heng Ji
Abstract
Modern large-scale Pre-trained Language Models (PLMs) have achieved tremendous success on a wide range of downstream tasks. However, most of the LM pre-training objectives only focus on text reconstruction, but have not sought to learn latent-level interpretable representations of sentences. In this paper, we manage to push the language models to obtain a deeper understanding of sentences by proposing a new pre-training objective, Sparse Latent Typing, which enables the model to sparsely extract sentence-level keywords with diverse latent types. Experimental results show that our model is able to learn interpretable latent type categories in a self-supervised manner without using any external knowledge. Besides, the language model pre-trained with such an objective also significantly improves Information Extraction related downstream tasks in both supervised and few-shot settings. Our code is publicly available at https://github.com/renll/SparseLT.- Anthology ID:
- 2022.emnlp-main.96
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1480–1494
- Language:
- URL:
- https://preview.aclanthology.org/icon-24-ingestion/2022.emnlp-main.96/
- DOI:
- 10.18653/v1/2022.emnlp-main.96
- Cite (ACL):
- Liliang Ren, Zixuan Zhang, Han Wang, Clare Voss, ChengXiang Zhai, and Heng Ji. 2022. Language Model Pre-Training with Sparse Latent Typing. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 1480–1494, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- Language Model Pre-Training with Sparse Latent Typing (Ren et al., EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/icon-24-ingestion/2022.emnlp-main.96.pdf