Graph Narrator

Graph Narrator aims at automatically generating natural language descriptions of subgraphs in knowledge graphs. The training corpus we use is Wikipedia article, and the knowledge graph we use is processed Freebase in which reverse triples and mediator nodes are removed.

Dataset

The dataset can be downloaded from here

The Wikipedia title to Freebase entity mid mapping file can be downloaded from here

Setup

We recommend createv Conda enviroment to run the code. To create the Conda enviroment, run

./setup_environment.sh

Preprocessing

Unzip the dataset folder, run

python graphnarrator/data/generate_input_graphnarrator.py dataset_xml_folder_path

Finetuning

To fine-tune the T5 model on graph narrator dataset, run

./graphnarrator/finetune_t5.sh t5-<small/base/large> gpu_id

Example

./graphnarrator/finetune_t5.sh t5-large 0

To fine-tune the BART model on graph narrator dataset, run

./graphnarrator/finetune_bart.sh bart-<base/large> gpu_id

Example

./graphnarrator/finetune_bart.sh bart-large 1

Decoding

To decode the T5 model that has been fine-tuned on graph narrator dataset, run

./graphnarrator/test_t5.sh fine-tuned_model_path gpu_id

Example

./graphnarrator/test_t5.sh /graphnarrator/t5-large-trim/best_tfmr 2

To decode the BART model that has been fine-tuned on graph narrator dataset, run

./graphnarrator/test_bart.sh fine-tuned_model_path gpu_id

Example

./graphnarrator/test_bart.sh /graphnarrator/bart-large-trim/best_tfmr 3

Trained Models

The T5-large model trained on Graph Narrator without sentence trimmer and then finetuned on WebNLG dataset can be downloaded from here

The T5-large model trained on Graph Narrator with sentence trimmer and then finetuned on WebNLG dataset can be downloaded from here