  We study the coarse-grained selection module in retrieval-based chatbot.
Coarse-grained selection is a basic module in a retrieval-based chatbot, which
constructs a rough candidate set from the whole database to speed up the
interaction with customers. So far, there are two kinds of approaches for
coarse-grained selection module: (1) sparse representation; (2) dense
representation. To the best of our knowledge, there is no systematic comparison
between these two approaches in retrieval-based chatbots, and which kind of
method is better in real scenarios is still an open question. In this paper, we
first systematically compare these two methods from four aspects: (1)
effectiveness; (2) index stoarge; (3) search time cost; (4) human evaluation.
Extensive experiment results demonstrate that dense representation method
significantly outperforms the sparse representation, but costs more time and
storage occupation. In order to overcome these fatal weaknesses of dense
representation method, we propose an ultra-fast, low-storage, and highly
effective Deep Semantic Hashing Coarse-grained selection method, called DSHC
model. Specifically, in our proposed DSHC model, a hashing optimizing module
that consists of two autoencoder models is stacked on a trained dense
representation model, and three loss functions are designed to optimize it. The
hash codes provided by hashing optimizing module effectively preserve the rich
semantic and similarity information in dense vectors. Extensive experiment
results prove that, our proposed DSHC model can achieve much faster speed and
lower storage than sparse representation, with limited performance loss
compared with dense representation. Besides, our source codes have been
publicly released for future research.
