BERTwich: Extending BERT’s Capabilities to Model Dialectal and Noisy Text

Aarohi Srivastava, David Chiang

Submission Type: Regular Long Paper

Submission Track: Multilinguality and Linguistic Diversity

Submission Track 2: Language Modeling and Analysis of Language Models

Keywords: BERT, language modeling, dialects, noisy text, fine-tuning

TL;DR: We introduce the novel idea of sandwiching BERT’s encoder stack between additional encoder layers trained to perform masked language modeling on noisy text in order to extend BERT’s capabilities to modeling dialectal and noisy text.

Abstract: Real-world NLP applications often deal with nonstandard text (e.g., dialectal, informal, or misspelled text). However, language models like BERT deteriorate in the face of dialect variation or noise. How do we push BERT’s modeling capabilities to encompass nonstandard text? Fine-tuning helps, but it is designed for specializing a model to a task and does not seem to bring about the deeper, more pervasive changes needed to adapt a model to nonstandard language. In this paper, we introduce the novel idea of sandwiching BERT's encoder stack between additional encoder layers trained to perform masked language modeling on noisy text. We find that our approach, paired with recent work on including character-level noise in fine-tuning data, can promote zero-shot transfer to dialectal text, as well as reduce the distance in the embedding space between words and their noisy counterparts.

Submission Number: 4704