  Word embeddings are a fixed, distributional representation of the context of
words in a corpus learned from word co-occurrences. While word embeddings have
proven to have many practical uses in natural language processing tasks, they
reflect the attributes of the corpus upon which they are trained. Recent work
has demonstrated that post-processing of word embeddings to apply information
found in lexical dictionaries can improve their quality. We build on this
post-processing technique by making it interactive. Our approach makes it
possible for humans to adjust portions of a word embedding space by moving sets
of words closer to one another. One motivating use case for this capability is
to enable users to identify and reduce the presence of bias in word embeddings.
Our approach allows users to trigger selective post-processing as they interact
with and assess potential bias in word embeddings.

Computation and Language;Machine Learning