  Word embeddings are a fixed, distributional representation of the context of
words in a corpus learned from word co-occurrences. Despite their proven
utility in machine learning tasks, word embedding models may capture uneven
semantic and syntactic representations, and can inadvertently reflect various
kinds of bias present within corpora upon which they were trained. It has been
demonstrated that post-processing of word embeddings to apply information found
in lexical dictionaries can improve the semantic associations, thus improving
their quality. Building on this idea, we propose a system that incorporates an
adaptation of word embedding post-processing, which we call "interactive
refitting", to address some of the most daunting qualitative problems found in
word embeddings. Our approach allows a human to identify and address potential
quality issues with word embeddings interactively. This has the advantage of
negating the question of who decides what constitutes bias or what other
quality issues may affect downstream tasks. It allows each organization or
entity to address concerns they may have at a fine grained level and to do so
in an iterative and interactive fashion. It also allows for better insight into
what effect word embeddings, and refinements to word embeddings, have on
machine learning pipelines.
