  A reverse dictionary takes the description of a target word as input and
outputs the target word together with other words that match the description.
Existing reverse dictionary methods cannot deal with highly variable input
queries and low-frequency target words successfully. Inspired by the
description-to-word inference process of humans, we propose the multi-channel
reverse dictionary model, which can mitigate the two problems simultaneously.
Our model comprises a sentence encoder and multiple predictors. The predictors
are expected to identify different characteristics of the target word from the
input query. We evaluate our model on English and Chinese datasets including
both dictionary definitions and human-written descriptions. Experimental
results show that our model achieves the state-of-the-art performance, and even
outperforms the most popular commercial reverse dictionary system on the
human-written description dataset. We also conduct quantitative analyses and a
case study to demonstrate the effectiveness and robustness of our model. All
the code and data of this work can be obtained on
https://github.com/thunlp/MultiRD.
