Language Model Pre-training for Hierarchical Document Representations

Ming-Wei Chang, Kristina Toutanova, Kenton Lee, Jacob Devlin

Abstract:

Hierarchical neural architectures can efficiently capture long-distance dependencies and have been used for many document-level tasks such as summarization, document segmentation, and fine-grained sentiment analysis. However, effective usage of such a large context can difficult to learn, especially in the case where there is limited labeled data available.
Building on the recent success of language model pretraining methods for learning flat representations of text, we propose algorithms for pre-training hierarchical document representations from unlabeled data. Unlike prior work, which has focused on pre-training contextual token representations or context-independent sentence/paragraph representations, our hierarchical document representations include fixed-length sentence/paragraph representations which integrate contextual information from the entire documents. Experiments on document segmentation, document-level question answering, and extractive document summarization demonstrate the effectiveness of the proposed pre-training algorithms.

Data: [TriviaQA](https://paperswithcode.com/dataset/triviaqa)

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:1901.09128/code)