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Dual-Encoders is a promising mechanism for answer retrieval in questionanswering (QA) systems. Currently most conventional Dual-Encoders learn thesemantic representations of questions and answers merely through matchingscore. Researchers proposed to introduce the QA interaction features in scoringfunction but at the cost of low efficiency in inference stage. To keepindependent 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 enhanceQA interaction in representation learning in training stage. However, the needsof text generation and answer retrieval are different, which leads to hardnessin training. In this work, we propose a framework to enhance the Dual-Encodersmodel with question answer cross-embeddings and a novel Geometry AlignmentMechanism (GAM) to align the geometry of embeddings from Dual-Encoders withthat from Cross-Encoders. Extensive experimental results show that ourframework significantly improves Dual-Encoders model and outperforms thestate-of-the-art method on multiple answer retrieval datasets.

Enhancing Dual-Encoders with Question and Answer Cross-Embeddings for Answer Retrieval