  Dual-Encoders is a promising mechanism for answer retrieval in question
answering (QA) systems. Currently most conventional Dual-Encoders learn the
semantic representations of questions and answers merely through matching
score. Researchers proposed to introduce the QA interaction features in scoring
function but at the cost of low efficiency in inference stage. To keep
independent encoding of questions and answers during inference stage,
variational auto-encoder is further introduced to reconstruct answers
(questions) from question (answer) embeddings as an auxiliary task to enhance
QA interaction in representation learning in training stage. However, the needs
of text generation and answer retrieval are different, which leads to hardness
in training. In this work, we propose a framework to enhance the Dual-Encoders
model with question answer cross-embeddings and a novel Geometry Alignment
Mechanism (GAM) to align the geometry of embeddings from Dual-Encoders with
that from Cross-Encoders. Extensive experimental results show that our
framework significantly improves Dual-Encoders model and outperforms the
state-of-the-art method on multiple answer retrieval datasets.
