SLM: Learning a Discourse Language Representation with Sentence Unshuffling

TL;DR

This paper proposes Sentence-level Language Modeling, a self-supervised pre-training method that enhances discourse-level representations by shuffling and reconstructing sentence order, improving downstream NLP task performance.

Contribution

It introduces a novel sentence shuffling pre-training objective to better capture intermediate discourse structures in language models.

Findings

Improved performance on GLUE, SQuAD, and DiscoEval tasks.

Enhanced sentence-level and discourse representations.

Outperforms original BERT significantly.

Abstract

We introduce Sentence-level Language Modeling, a new pre-training objective for learning a discourse language representation in a fully self-supervised manner. Recent pre-training methods in NLP focus on learning either bottom or top-level language representations: contextualized word representations derived from language model objectives at one extreme and a whole sequence representation learned by order classification of two given textual segments at the other. However, these models are not directly encouraged to capture representations of intermediate-size structures that exist in natural languages such as sentences and the relationships among them. To that end, we propose a new approach to encourage learning of a contextualized sentence-level representation by shuffling the sequence of input sentences and training a hierarchical transformer model to reconstruct the original ordering. Through experiments on downstream tasks such as GLUE, SQuAD, and DiscoEval, we show that this feature of our model improves the performance of the original BERT by large margins.