A Unified Model for Reverse Dictionary and Definition Modelling (2022)

Abstract: We build a dual-way neural dictionary to retrieve words given definitions, and produce definitions for queried words. The model learns the two tasks simultaneously and handles unknown words via embeddings. It casts a word or a definition to the same representation space through a shared layer, then generates the other form in a multi-task fashion. Our method achieves promising automatic scores on previous benchmarks without extra resources. Human annotators prefer the model’s outputs in both reference-less and reference-based evaluation, indicating its practicality. Analysis suggests that multiple objectives benefit learning.

## introduction a monolingual dictionary is a large-scale collection of words paired with their definitions. the main use of such a resource is to find a word or a definition having known the other. formally, the task of generating a textual definition from a word is named definition modelling; the inverse task of retrieving a word given a definition is called reverse dictionary. lately, the two tasks are approached using neural networks @xcite , and in turn they help researchers better understand word sense and embeddings. research can further benefit low-resource languages where high-quality dictionaries are not available @xcite . finally, practical applications include language education, writing assistance, semantic search, etc. while previous works solve one problem at a time, we argue that both tasks can be learned and dealt with concurrently, based on the intuition that a word and its definition share the same meaning. we design a neural model to embed words and definitions into a shared semantic space, and generate them from this space. consequently, the training paradigm can include reconstruction and embedding similarity tasks. such a system can be viewed as a neural dictionary that supports two-way index-ing and querying. in our experiments, jointly learning both tasks does not increase the total model size, yet demonstrates ease and effectiveness. our code is publicly available. foot_0 ## related work although research on the two tasks can be traced back to the early 2000s, recent research has shifted towards neural networks, which we describe here. @xcite pioneer the use of rnn and bag-of-words models to convert texts to word vectors, on top of which @xcite add an extra word category classifier. pilehvar (2019) integrates super-sense into target embeddings to disambiguate polysemous words. @xcite design a multichannel network to predict a word with its features like category, pos tag, morpheme, sememe, etc. ## conclusion we build a multi-task model for reverse dictionary and definition modelling. the approach records strong numbers on public datasets. our method delegates disambiguation to bert and minimizes dependency on linguistically annotated resources, so it can potentially be made cross-lingual and multilingual. a limitation is that the current evaluation centers on english, without exploring low-resource languages, which could be impactful extensions that benefit the community.