Abstract
We introduce the cross-match test - an exact, distribution free, high-dimensional hypothesis test as an intrinsic evaluation metric for word embeddings. We show that cross-match is an effective means of measuring the distributional similarity between different vector representations and of evaluating the statistical significance of different vector embedding models. Additionally, we find that cross-match can be used to provide a quantitative measure of linguistic similarity for selecting bridge languages for machine translation. We demonstrate that the results of the hypothesis test align with our expectations and note that the framework of two sample hypothesis testing is not limited to word embeddings and can be extended to all vector representations.- Anthology ID:
- W17-5303
- Volume:
- Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for NLP
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Venue:
- RepEval
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 16–20
- Language:
- URL:
- https://aclanthology.org/W17-5303
- DOI:
- 10.18653/v1/W17-5303
- Cite (ACL):
- Nishant Gurnani. 2017. Hypothesis Testing based Intrinsic Evaluation of Word Embeddings. In Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for NLP, pages 16–20, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Hypothesis Testing based Intrinsic Evaluation of Word Embeddings (Gurnani, RepEval 2017)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/W17-5303.pdf