  Answer selection aims at identifying the correct answer for a given question
from a set of potentially correct answers. Contrary to previous works, which
typically focus on the semantic similarity between a question and its answer,
our hypothesis is that question-answer pairs are often in analogical relation
to each other. Using analogical inference as our use case, we propose a
framework and a neural network architecture for learning dedicated sentence
embeddings that preserve analogical properties in the semantic space. We
evaluate the proposed method on benchmark datasets for answer selection and
demonstrate that our sentence embeddings indeed capture analogical properties
better than conventional embeddings, and that analogy-based question answering
outperforms a comparable similarity-based technique.
