# Rare Words: A Major Problem for Contextualized Embeddings And How to Fix it by Attentive Mimicking

## TL;DR

This paper addresses the challenge of rare words in deep language models like BERT by adapting Attentive Mimicking with a new one-token approximation, significantly improving their understanding of rare words without task-specific fine-tuning.

## Contribution

The authors adapt Attentive Mimicking for deep language models with subword tokenization using a novel one-token approximation, enhancing rare word understanding.

## Key findings

- Adding the method improves BERT's rare word comprehension
- The approach outperforms baseline models on the new dataset
- Rare word semantic properties are better captured after adaptation

## Abstract

Pretraining deep neural network architectures with a language modeling objective has brought large improvements for many natural language processing tasks. Exemplified by BERT, a recently proposed such architecture, we demonstrate that despite being trained on huge amounts of data, deep language models still struggle to understand rare words. To fix this problem, we adapt Attentive Mimicking, a method that was designed to explicitly learn embeddings for rare words, to deep language models. In order to make this possible, we introduce one-token approximation, a procedure that enables us to use Attentive Mimicking even when the underlying language model uses subword-based tokenization, i.e., it does not assign embeddings to all words. To evaluate our method, we create a novel dataset that tests the ability of language models to capture semantic properties of words without any task-specific fine-tuning. Using this dataset, we show that adding our adapted version of Attentive Mimicking to BERT does indeed substantially improve its understanding of rare words.