Abstract
This paper evaluates morphology-based embeddings for English and Russian languages. Despite the interest and introduction of several morphology based word embedding models in the past and acclaimed performance improvements on word similarity and language modeling tasks, in our experiments, we did not observe any stable preference over two of our baseline models - SkipGram and FastText. The performance exhibited by morphological embeddings is the average of the two baselines mentioned above.- Anthology ID:
- W19-2010
- Volume:
- Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, USA
- Editors:
- Anna Rogers, Aleksandr Drozd, Anna Rumshisky, Yoav Goldberg
- Venue:
- RepEval
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 77–81
- Language:
- URL:
- https://aclanthology.org/W19-2010
- DOI:
- 10.18653/v1/W19-2010
- Cite (ACL):
- Vitaly Romanov and Albina Khusainova. 2019. Evaluation of Morphological Embeddings for English and Russian Languages. In Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP, pages 77–81, Minneapolis, USA. Association for Computational Linguistics.
- Cite (Informal):
- Evaluation of Morphological Embeddings for English and Russian Languages (Romanov & Khusainova, RepEval 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/W19-2010.pdf