ERNIE-DOC: The Retrospective Long-Document Modeling Transformer

• URL: http://arxiv.org/abs/2012.15688v1
• Date: Thu, 31 Dec 2020 16:12:48 GMT
• Title: ERNIE-DOC: The Retrospective Long-Document Modeling Transformer
• Authors: Siyu Ding, Junyuan Shang, Shuohuan Wang, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang
• Abstract summary: We propose ERNIE-DOC, a document-level language pretraining model based on Recurrence Transformers. Two well-designed techniques, namely the retrospective feed mechanism and the enhanced recurrence mechanism enable ERNIE-DOC with much longer effective context length. Various experiments on both English and Chinese document-level tasks are conducted.
• Score: 24.426571160930635
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transformers are not suited for processing long document input due to its quadratically increasing memory and time consumption. Simply truncating a long document or applying the sparse attention mechanism will incur the context fragmentation problem or inferior modeling capability with comparable model size. In this paper, we propose ERNIE-DOC, a document-level language pretraining model based on Recurrence Transformers. Two well-designed techniques, namely the retrospective feed mechanism and the enhanced recurrence mechanism enable ERNIE-DOC with much longer effective context length to capture the contextual information of a whole document. We pretrain ERNIE-DOC to explicitly learn the relationship among segments with an additional document-aware segment reordering objective. Various experiments on both English and Chinese document-level tasks are conducted. ERNIE-DOC achieves SOTA language modeling result of 16.8 ppl on WikiText-103 and outperforms competitive pretraining models on most language understanding tasks such as text classification, question answering by a large margin.

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