Bad Form: Comparing Context-Based and Form-Based Few-Shot Learning in Distributional Semantic Models
Abstract
Word embeddings are an essential component in a wide range of natural language processing applications. However, distributional semantic models are known to struggle when only a small number of context sentences are available. Several methods have been proposed to obtain higher-quality vectors for these words, leveraging both this context information and sometimes the word forms themselves through a hybrid approach. We show that the current tasks do not suffice to evaluate models that use word-form information, as such models can easily leverage word forms in the training data that are related to word forms in the test data. We introduce 3 new tasks, allowing for a more balanced comparison between models. Furthermore, we show that hyperparameters that have largely been ignored in previous work can consistently improve the performance of both baseline and advanced models, achieving a new state of the art on 4 out of 6 tasks.- Anthology ID:
- D19-6104
- Volume:
- Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Editors:
- Colin Cherry, Greg Durrett, George Foster, Reza Haffari, Shahram Khadivi, Nanyun Peng, Xiang Ren, Swabha Swayamdipta
- Venue:
- WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 31–39
- Language:
- URL:
- https://aclanthology.org/D19-6104
- DOI:
- 10.18653/v1/D19-6104
- Cite (ACL):
- Jeroen Van Hautte, Guy Emerson, and Marek Rei. 2019. Bad Form: Comparing Context-Based and Form-Based Few-Shot Learning in Distributional Semantic Models. In Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019), pages 31–39, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- Bad Form: Comparing Context-Based and Form-Based Few-Shot Learning in Distributional Semantic Models (Van Hautte et al., 2019)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/D19-6104.pdf