SLM: Learning a Discourse Language Representation with Sentence Unshuffling

Haejun Lee, Drew A. Hudson, Kangwook Lee, Christopher D. Manning


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.
Anthology ID:
2020.emnlp-main.120
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1551–1562
Language:
URL:
https://www.aclweb.org/anthology/2020.emnlp-main.120
DOI:
10.18653/v1/2020.emnlp-main.120
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.emnlp-main.120.pdf