  We introduce POLAR - a framework that adds interpretability to pre-trained
word embeddings via the adoption of semantic differentials. Semantic
differentials are a psychometric construct for measuring the semantics of a
word by analysing its position on a scale between two polar opposites (e.g.,
cold -- hot, soft -- hard). The core idea of our approach is to transform
existing, pre-trained word embeddings via semantic differentials to a new
"polar" space with interpretable dimensions defined by such polar opposites.
Our framework also allows for selecting the most discriminative dimensions from
a set of polar dimensions provided by an oracle, i.e., an external source. We
demonstrate the effectiveness of our framework by deploying it to various
downstream tasks, in which our interpretable word embeddings achieve a
performance that is comparable to the original word embeddings. We also show
that the interpretable dimensions selected by our framework align with human
judgement. Together, these results demonstrate that interpretability can be
added to word embeddings without compromising performance. Our work is relevant
for researchers and engineers interested in interpreting pre-trained word
embeddings.
