Multi-Channel Reverse Dictionary Model

Abstract

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.

WantWords: An Open-source Online Reverse Dictionary System

“…To tackle this issue of the NLM-based method, we proposed a multi-channel reverse dictionary model (Zhang et al, 2020). This model is composed of a sentence encoder, more specifically, a bi-directional LSTM (BiLSTM) (Hochreiter and Schmidhuber, 1997) with attention (Bahdanau et al, 2015), and four characteristic predictors.…”

“…4 As for fine-tuning (training) for English, we use the dictionary definition dataset created by Hill et al (2016), which contains about 100, 000 words and 900, 000 worddefinition pairs extracted from five dictionaries. For fine-tuning (training) for Chinese, we build a large-scale dictionary definition dataset based on the dataset created by Zhang et al (2020). It contains 137, 174 words and 270, 549 word-definition pairs, where the definitions are extracted from several authoritative Chinese dictionaries including Modern Chinese Dictionary, Xinhua Dictionary and Chinese Idiom Dictionary as well as an opensource dictionary dataset.…”

“…It contains 137, 174 words and 270, 549 word-definition pairs, where the definitions are extracted from several authoritative Chinese dictionaries including Modern Chinese Dictionary, Xinhua Dictionary and Chinese Idiom Dictionary as well as an opensource dictionary dataset. 5 MRDM requires some other resources, and we simply follow the settings in Zhang et al (2020). Specifically, for English, we use Morfessor (Virpioja et al, 2013) to segment words into morphemes, WordNet (Miller, 1995) to obtain PoS and word category information, and OpenHowNet 6 (Qi et al, 2019) to obtain sememe information.…”

“…As for Chinese, we use three test sets: (1) Definition set, which contains 2, 000 pairs of words and dictionary definitions that are selected at random and do not exist in the training set; (2) Description set, which is composed of 200 word-description pairs given by Chinese native speakers and is built by Zhang et al (2020); and (3) Question set, which collects 272 real-world Chinese exam questionanswers of writing the right word given a description from the Internet and is also created by Zhang et al (2020).…”

“…To solve these problems, we design and develop a new online reverse dictionary system named WantWords, which is totally open-source. WantWords is mainly based on our proposed multi-channel reverse dictionary model (Zhang et al, 2020), which achieves state-of-the-art performance on an English benchmark dataset. Our system uses an improved version of the multi-channel reverse dictionary model and incorporates some engineering tricks to handle extreme cases.…”