Abstract
While pre-trained word embeddings have been shown to improve the performance of downstream tasks, many questions remain regarding their reliability: Do the same pre-trained word embeddings result in the best performance with slight changes to the training data? Do the same pre-trained embeddings perform well with multiple neural network architectures? Do imputation strategies for unknown words impact reliability? In this paper, we introduce two new metrics to understand the downstream reliability of word embeddings. We find that downstream reliability of word embeddings depends on multiple factors, including, the evaluation metric, the handling of out-of-vocabulary words, and whether the embeddings are fine-tuned.- Anthology ID:
- 2020.coling-main.299
- Volume:
- Proceedings of the 28th International Conference on Computational Linguistics
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Editors:
- Donia Scott, Nuria Bel, Chengqing Zong
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 3371–3388
- Language:
- URL:
- https://aclanthology.org/2020.coling-main.299
- DOI:
- 10.18653/v1/2020.coling-main.299
- Cite (ACL):
- Anthony Rios and Brandon Lwowski. 2020. An Empirical Study of the Downstream Reliability of Pre-Trained Word Embeddings. In Proceedings of the 28th International Conference on Computational Linguistics, pages 3371–3388, Barcelona, Spain (Online). International Committee on Computational Linguistics.
- Cite (Informal):
- An Empirical Study of the Downstream Reliability of Pre-Trained Word Embeddings (Rios & Lwowski, COLING 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.coling-main.299.pdf
- Data
- OLID