Graph based Neural Sentence Ordering

Installation

The following packages are needed:

• Python == 3.6
• Pytorch >= 1.0
• torchtext == 0.3
• Stanford POS tagger or Dependency Parser
• Glove (100 dim)

Dataset Format

*.lower: each line is a document: sentence_0 sentence_1 sentence_2

*.eg: entity1:i-r means entity1 is in the sentence_i and its role is r.

Other datasets are easy to access and process. We also recommand a high-quality dataset for sentence ordering, ROC story.

Preprocessing

Use a dependency parser to get POS and syntax

Select the word as entity if the POS is noun

Find the nsubj and dobj to get the roles ( or just use a POS tagger and ignore the roles if you think the dependency parser is time-consuming)

Training and Evaluation

bash run.sh