REVIEW 1 cited by
Multimodal Word Distributions
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Multimodal Word Distributions
read the original abstract
Word embeddings provide point representations of words containing useful semantic information. We introduce multimodal word distributions formed from Gaussian mixtures, for multiple word meanings, entailment, and rich uncertainty information. To learn these distributions, we propose an energy-based max-margin objective. We show that the resulting approach captures uniquely expressive semantic information, and outperforms alternatives, such as word2vec skip-grams, and Gaussian embeddings, on benchmark datasets such as word similarity and entailment.
Forward citations
Cited by 1 Pith paper
-
TaxoBell: Gaussian Box Embeddings for Self-Supervised Taxonomy Expansion
TaxoBell, a Gaussian-box embedding model with symmetric and asymmetric energy losses, ranks parent concepts more accurately than seven taxonomy-expansion baselines on five benchmarks.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.