We present a locality preserving loss (LPL) that improves the alignment between vector space embeddings while separating uncorrelated representations. Given two pretrained embedding manifolds, LPL optimizes a model to project an embedding and maintain its local neighborhood while aligning one manifold to another. This reduces the overall size of the dataset required to align the two in tasks such as crosslingual word alignment. We show that the LPL-based alignment between input vector spaces acts as a regularizer, leading to better and consistent accuracy than the baseline, especially when the size of the training set is small. We demonstrate the effectiveness of LPL-optimized alignment on semantic text similarity (STS), natural language inference (SNLI), multi-genre language inference (MNLI) and cross-lingual word alignment (CLA) showing consistent improvements, finding up to 16% improvement over our baseline in lower resource settings.

We present a gold standard for evaluating scale membership and the order of scalar adjectives. In addition to evaluating existing methods of ordering adjectives, this knowledge will aid in studying the organization of adjectives in the lexicon. This resource is the result of two elicitation tasks conducted with informants from Amazon Mechanical Turk. The first task is notable for gathering open-ended lexical data from informants. The data is analyzed using Cultural Consensus Theory, a framework from anthropology, to not only determine scale membership but also the level of consensus among the informants (Romney et al., 1986). The second task gathers a culturally salient ordering of the words determined to be members. We use this method to produce 12 scales of adjectives for use in evaluation.