Lacking the embedding of a word? Look it up into a traditional dictionary
This work addresses a specific bottleneck in natural language processing for rare words, offering an incremental improvement by leveraging existing dictionary resources.
The paper tackles the problem of generating word embeddings for rare words, which are poorly handled by standard embedding methods, by using definitions from traditional dictionaries. The proposed methods, DefiNNet and DefBERT, significantly outperform state-of-the-art and baseline methods, with DefiNNet beating FastText and DefBERT outperforming BERT for out-of-vocabulary words.
Word embeddings are powerful dictionaries, which may easily capture language variations. However, these dictionaries fail to give sense to rare words, which are surprisingly often covered by traditional dictionaries. In this paper, we propose to use definitions retrieved in traditional dictionaries to produce word embeddings for rare words. For this purpose, we introduce two methods: Definition Neural Network (DefiNNet) and Define BERT (DefBERT). In our experiments, DefiNNet and DefBERT significantly outperform state-of-the-art as well as baseline methods devised for producing embeddings of unknown words. In fact, DefiNNet significantly outperforms FastText, which implements a method for the same task-based on n-grams, and DefBERT significantly outperforms the BERT method for OOV words. Then, definitions in traditional dictionaries are useful to build word embeddings for rare words.