SLM: Learning a Discourse Language Representation with Sentence Unshuffling
This addresses the need for better intermediate-size structure representations in NLP, offering a novel self-supervised method for discourse understanding, though it builds upon existing transformer architectures.
The paper tackles the problem of learning discourse-level language representations by proposing a new pre-training objective called Sentence-level Language Modeling, which involves shuffling input sentences and training a hierarchical transformer to reconstruct the original order, resulting in improved performance over BERT on tasks like GLUE, SQuAD, and DiscoEval with large margins.
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.