Consistent Alignment of Word Embedding Models
This work addresses a specific challenge in NLP for improving word embedding consistency, but it appears incremental as it builds on known linear relationships in embedding spaces.
The paper tackles the problem of aligning variations of word embedding models in a joint latent space using synthetic data, resulting in substantial improvements in recovering embeddings of local neighborhoods.
Word embedding models offer continuous vector representations that can capture rich contextual semantics based on their word co-occurrence patterns. While these word vectors can provide very effective features used in many NLP tasks such as clustering similar words and inferring learning relationships, many challenges and open research questions remain. In this paper, we propose a solution that aligns variations of the same model (or different models) in a joint low-dimensional latent space leveraging carefully generated synthetic data points. This generative process is inspired by the observation that a variety of linguistic relationships is captured by simple linear operations in embedded space. We demonstrate that our approach can lead to substantial improvements in recovering embeddings of local neighborhoods.