CharBERT: Character-aware Pre-trained Language Model

TL;DR

CharBERT introduces a character-aware pre-trained language model that constructs contextual word embeddings from characters, enhancing robustness and performance over traditional subword-based models like BERT and RoBERTa.

Contribution

It proposes a novel heterogeneous interaction module and a new pre-training task (NLM) to improve character-level representations in PLMs.

Findings

Significantly improves performance on question answering, sequence labeling, and text classification.

Enhances robustness against adversarial misspellings.

Outperforms previous models in both accuracy and robustness metrics.

Abstract

Most pre-trained language models (PLMs) construct word representations at subword level with Byte-Pair Encoding (BPE) or its variations, by which OOV (out-of-vocab) words are almost avoidable. However, those methods split a word into subword units and make the representation incomplete and fragile. In this paper, we propose a character-aware pre-trained language model named CharBERT improving on the previous methods (such as BERT, RoBERTa) to tackle these problems. We first construct the contextual word embedding for each token from the sequential character representations, then fuse the representations of characters and the subword representations by a novel heterogeneous interaction module. We also propose a new pre-training task named NLM (Noisy LM) for unsupervised character representation learning. We evaluate our method on question answering, sequence labeling, and text classification tasks, both on the original datasets and adversarial misspelling test sets. The experimental results show that our method can significantly improve the performance and robustness of PLMs simultaneously. Pretrained models, evaluation sets, and code are available at https://github.com/wtma/CharBERT