Do Language Embeddings capture Scales?
Xikun Zhang, Deepak Ramachandran, Ian Tenney, Yanai Elazar, Dan Roth
Abstract
Pretrained Language Models (LMs) have been shown to possess significant linguistic, common sense and factual knowledge. One form of knowledge that has not been studied yet in this context is information about the scalar magnitudes of objects. We show that pretrained language models capture a significant amount of this information but are short of the capability required for general common-sense reasoning. We identify contextual information in pre-training and numeracy as two key factors affecting their performance, and show that a simple method of canonicalizing numbers can have a significant effect on the results.- Anthology ID:
- 2020.blackboxnlp-1.27
- Volume:
- Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- BlackboxNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 292–299
- Language:
- URL:
- https://aclanthology.org/2020.blackboxnlp-1.27
- DOI:
- 10.18653/v1/2020.blackboxnlp-1.27
- Cite (ACL):
- Xikun Zhang, Deepak Ramachandran, Ian Tenney, Yanai Elazar, and Dan Roth. 2020. Do Language Embeddings capture Scales?. In Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, pages 292–299, Online. Association for Computational Linguistics.
- Cite (Informal):
- Do Language Embeddings capture Scales? (Zhang et al., BlackboxNLP 2020)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2020.blackboxnlp-1.27.pdf