W ant W ords: An Open-source Online Reverse Dictionary System (2020)

Abstract: A reverse dictionary takes descriptions of words as input and outputs words semantically matching the input descriptions. Reverse dictionaries have great practical value such as solving the tip-of-the-tongue problem and helping new language learners. There have been some online reverse dictionary systems, but they support English reverse dictionary queries only and their performance is far from perfect. In this paper, we present a new open-source online reverse dictionary system named WantWords ( https://wantwords.thunlp.org/ ). It not only significantly outperforms other reverse dictionary systems on English reverse dictionary performance, but also supports Chinese and English-Chinese as well as Chinese-English cross-lingual reverse dictionary queries for the first time. Moreover, it has user-friendly front-end design which can help users find the words they need quickly and easily. All the code and data are available at https://github.com/thunlp/WantWords .

## introduction opposite to a regular (forward) dictionary that provides definitions for query words, a reverse dictionary @xcite returns words semantically matching the query descriptions. in figure 1 , for example, a regular dictionary tells you the definition of "expressway" is "a wide road that allows traffic to travel fast", while a reverse dictionary outputs "expressway" and other semantically similar words like "freeway" which match the query description "a road where cars go very quickly without stopping" you input. reverse dictionaries are useful in practical applications. first and foremost, they can effectively solve the tip-of-the-tongue problem (brown and * indicates equal contribution † work done during internship at tsinghua university figure 1 : an example illustrating what a regular (forward) dictionary and a reverse dictionary are. @xcite , namely the phenomenon of failing to retrieve a word from memory. many people frequently suffer the problem, especially those who write a lot such as writers, researchers and students. with the help of reverse dictionaries, people can quickly and easily find the words that they need but temporarily forget. in addition, reverse dictionaries are helpful to new language learners who grasp a limited number of words. they will know and learn some new words that have the meanings they want to express by using a reverse dictionary. also, reverse dictionaries can help word selection (or word dictionary) anomia patients, people who can recognize and describe an object but fail to name it due to neurological disorder @xcite . currently, there are mainly two online reverse dictionaries, namely onelook foot_0 and reversedictionary. 2 their performance is far from perfect. further, both of them are closed-source and only support english reverse dictionary queries. 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 @xcite , 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. evaluation results show that with these improvements, our system achieves higher performance. besides, our system supports chinese reverse dictionary queries and chinese-english as well as english-chinese cross-lingual reverse dictionary queries, all of which are realized for the first time. finally, our system is very user-friendly. it includes multiple filters and sort methods, and can automatically cluster the candidate words, all of which help users find the target words as quickly as possible. ## related work there are mainly two methods for reverse dictionary building. the first one is based on sentence matching @xcite @xcite . its main idea is to return the words whose dictionary definitions are most similar to the query description. although effective in some cases, this method cannot cope with the problem that human-written query descriptions might differ widely from dictionary definitions. the second method uses a neural language model (nlm) to encode the query description into a vector in the word embedding space, and returns the words with the closest embeddings to the vector of the query description @xcite @xcite @xcite . performance of this method depends largely on the quality of word embeddings. unfortunately, according to zipf's law @xcite , many words are low-frequency and usually have poor embeddings. to tackle this issue of the nlm-based method, we proposed a multi-channel reverse dictionary model @xcite . this model is composed of a sentence encoder, more specifically, a bi-directional lstm (bilstm) @xcite with attention @xcite , and four characteristic predictors. the four predictors are used to predict the part-ofspeech, morphemes, word category and sememes foot_2 of the target word according to the query description, respectively. the incorporation of the characteristic predictors can help find the target words with poor embeddings and exclude wrong words with similar embeddings to the target words, such query description translation cross-lingual dictionary multi-channel reverse dictionary model query length mode cross-lingual (en-zh/zh-en) monolingual (en/zh) =1 (word) >1 (sentence) word similarity confidence score query @xmath0. experimental results have demonstrated that our multi-channel reverse dictionary model achieves state-of-the-art performance. in wantwords, we employ an improved version of it that yields better results. ## system architecture in this section, we describe the system architecture of wantwords. we first give an overview of its workflow, then we detail the improved multichannel reverse dictionary model, and finally we introduce its front-end design. ## evaluation in this section, we evaluate the reverse dictionary performance of wantwords. we conduct both monolingual (english and chinese) and crosslingual (english-chinese and chinese-english) reverse dictionary evaluations. ## conclusion and future work in this paper, we present wantwords, an online reverse dictionary system, which achieves stateof-the-art performance on an english reverse dictionary benchmark dataset. besides, it supports chinese and english-chinese as well as chinese-english cross-lingual reverse dictionary queries for the first time. in the future, we will try to incorporate multi-word expressions and idioms in the system. also, we will work on improving crosslingual reverse dictionary performance by bilingual word embeddings or multilingual bert.