TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations

Anonymous

Keywords: BERT, Twitter, Tweet, Embedding, Language Model, Transformer

Abstract: We present TwHIN-BERT, a multilingual language model trained on in-domain data from the popular social network Twitter. TwHIN-BERT differs from prior pre-trained language models as it is trained with not only text-based self-supervision, but also with a social objective based on the rich social engagements within a Twitter heterogeneous information network (TwHIN).Our model is trained on $7$ billion tweets covering over $100$ distinct languages providing a valuable representation to model short, noisy, user-generated text. We evaluate our model on a variety of multilingual social recommendation and semantic understanding tasks and demonstrate significant metric improvement over established pre-trained language models. We will freely open-source TwHIN-BERT and our curated hashtag prediction and social engagement benchmark datasets to the research community.

Paper Type: long

Research Area: Machine Learning for NLP