  Recognizing relations between entities is a pivotal task of relational
learning. Learning relation representations from distantly-labeled datasets is
difficult because of the abundant label noise and complicated expressions in
human language. This paper aims to learn predictive, interpretable, and robust
relation representations from distantly-labeled data that are effective in
different settings, including supervised, distantly supervised, and few-shot
learning. Instead of solely relying on the supervision from noisy labels, we
propose to learn prototypes for each relation from contextual information to
best explore the intrinsic semantics of relations. Prototypes are
representations in the feature space abstracting the essential semantics of
relations between entities in sentences. We learn prototypes based on
objectives with clear geometric interpretation, where the prototypes are unit
vectors uniformly dispersed in a unit ball, and statement embeddings are
centered at the end of their corresponding prototype vectors on the surface of
the ball. This approach allows us to learn meaningful, interpretable prototypes
for the final classification. Results on several relation learning tasks show
that our model significantly outperforms the previous state-of-the-art models.
We further demonstrate the robustness of the encoder and the interpretability
of prototypes with extensive experiments.
