Hypothesis Testing based Intrinsic Evaluation of Word Embeddings

Nishant Gurnani


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
Venues:
RepEval | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
16–20
Language:
URL:
https://www.aclweb.org/anthology/W17-5303
DOI:
10.18653/v1/W17-5303
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-5303.pdf