Analysis of word embedding properties to inform their use in downstream NLP tasks has largely been studied by assessing nearest neighbors. However, geometric properties of the continuous feature space contribute directly to the use of embedding features in downstream models, and are largely unexplored. We consider four properties of word embedding geometry, namely: position relative to the origin, distribution of features in the vector space, global pairwise distances, and local pairwise distances. We define a sequence of transformations to generate new embeddings that expose subsets of these properties to downstream models and evaluate change in task performance to understand the contribution of each property to NLP models. We transform publicly available pretrained embeddings from three popular toolkits (word2vec, GloVe, and FastText) and evaluate on a variety of intrinsic tasks, which model linguistic information in the vector space, and extrinsic tasks, which use vectors as input to machine learning models. We find that intrinsic evaluations are highly sensitive to absolute position, while extrinsic tasks rely primarily on local similarity. Our findings suggest that future embedding models and post-processing techniques should focus primarily on similarity to nearby points in vector space.
Word embedding models and outputs have been analyzed from several angles. In terms of performance, evaluating the quality'' of word embedding models has long been a thorny problem. While intrinsic evaluations such as word similarity and analogy completion are intuitive and easy to compute, they are limited by both confounding geometric factors @cite_16 and task-specific factors @cite_28 @cite_0 . show that these tasks, while correlated with some semantic content, do not always predict downstream performance. Thus, it is necessary to use a more comprehensive set of intrinsic and extrinsic evaluations for embeddings.
@cite_28 @cite_16 @cite_0
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Lacking standardized extrinsic evaluation methods for vector representations of words, the NLP community has relied heavily on word similarity tasks as a proxy for intrinsic evaluation of word vectors. Word similarity evaluation, which correlates the distance between vectors and human judgments of “semantic similarity” is attractive, because it is computationally inexpensive and fast. In this paper we present several problems associated with the evaluation of word vectors on word similarity datasets, and summarize existing solutions. Our study suggests that the use of word similarity tasks for evaluation of word vectors is not sustainable and calls for further research on evaluation methods. The offset method for solving word analo- gies has become a standard evaluation tool for vector-space semantic models: it is considered desirable for a space to repre- sent semantic relations as consistent vec- tor offsets. We show that the method's re- liance on cosine similarity conflates offset consistency with largely irrelevant neigh- borhood structure, and propose simple baselines that should be used to improve the utility of the method in vector space evaluation.