Multi-channel Reverse Dictionary Model

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. 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. Introduction A regular (forward) dictionary maps words to definitions while a reverse dictionary (Sierra 2000) does the opposite and maps descriptions to corresponding words. In Figure 1, for example, a regular dictionary tells you that "expressway" is "a wide road that allows traffic to travel fast", and when you input "a road where cars go very quickly without stopping" to a reverse dictionary, it might return "expressway" together with other semantically similar words like "freeway". Reverse dictionaries have great practical value.