Abstract
This paper studies the effect of limited precision data representation and computation on word embeddings. We present a systematic evaluation of word embeddings with limited memory and discuss methods that directly train the limited precision representation with limited memory. Our results show that it is possible to use and train an 8-bit fixed-point value for word embedding without loss of performance in word/phrase similarity and dependency parsing tasks.
| Original language | English (US) |
|---|---|
| Title of host publication | 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers |
| Publisher | Association for Computational Linguistics (ACL) |
| Volume | 2 |
| ISBN (Electronic) | 9781510827585 |
| State | Published - 2016 |
| Event | 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Berlin, Germany Duration: Aug 7 2016 → Aug 12 2016 |
Other
| Other | 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 |
|---|---|
| Country/Territory | Germany |
| City | Berlin |
| Period | 8/7/16 → 8/12/16 |
ASJC Scopus subject areas
- Language and Linguistics
- Linguistics and Language