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Neural Sentence Ordering
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Sentence ordering is a general and critical task for natural language generation applications. Previous works have focused on improving its performance in an external, downstream task, such as multi-document summarization. Given its importance, we propose to study it as an isolated task. We collect a large corpus of academic texts, and derive a data driven approach to learn pairwise ordering of sentences, and validate the efficacy with extensive experiments. Source codes and dataset of this paper will be made publicly available.
Forward citations
Cited by 2 Pith papers
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Evaluation Benchmarks and Learning Criteria for Discourse-Aware Sentence Representations
DiscoEval is a new benchmark for measuring discourse awareness in sentence embeddings, and Wikipedia-structure training losses modestly change, but do not beat, pretrained encoders.
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StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding
Adding word-order and sentence-order reconstruction tasks to BERT pre-training improves performance on GLUE, SNLI, and SQuAD v1.1 benchmarks.
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