- TL;DR: We propose a novel representation learning framework that obtains a vector with an inverted attribute in embedding space without explicit attribute knowledge of the given word.
- Abstract: We propose a word attribute transfer framework based on reflection to obtain a word vector with an inverted target attribute for a given word in a word embedding space. Word embeddings based on Pointwise Mutual Information (PMI) represent such analogic relations as king - man + woman \approx queen. These relations can be used for changing a word’s attribute from king to queen by changing its gender. This attribute transfer can be performed by subtracting a difference vector man - woman from king when we have explicit knowledge of the gender of given word king. However, this knowledge cannot be developed for various words and attributes in practice. For transferring queen into king in this analogy-based manner, we need to know that queen denotes a female and add the difference vector to it. In this work, we transfer such binary attributes based on an assumption that such transfer mapping will become identity mapping when we apply it twice. We introduce a framework based on reflection mapping that satisfies this property; queen should be transferred back to king with the same mapping as the transfer from king to queen. Experimental results show that the proposed method can transfer the word attributes of the given words, and does not change the words that do not have the target attributes.
- Code: https://drive.google.com/open?id=1hbRZNnEoEKo55hJGO9qgUJ9FU--5heYE
- Keywords: embedding, representation learning, analogy, geometry