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line-chart-captioning

This repository is for creating a machine learning model that manages to create natural language descriptions for line graphs.

Dataset

This project uses two pre-existing datasets:

The FigureQA dataset is used to generate a synthetic dataset, while Chart-to-Text is used to generate a natural-language dataset.

Generating synthetic dataset

To generate the synthetic dataset, place the downloaded folders to data/figureqa (check the README there), and run

python3 src/synthetic/preprocess.py data/fiqureqa/X

Flags you can provide:

  • --unroll-descriptions: By default, if a plot/figure/graph has more than 1 description, they are concatenated. This flag unrolls the descriptions, to create n rows in captions.csv if there are n > 1 descriptions for a given graph.
  • --replace-subjects: Replaces subjects in descriptions. For example Red is greater than Blue becomes <A> is greater than <B>. This flag also adds subject_map column to captions.csv, so for every plot there is a JSON blob string that maps replacements to original subjects.
  • --description-limit N: Limits description length in sentences. Cannot be present together with --unroll-descriptions
  • --synthetic-config PATH_TO_FILE: Provide a config file for custom question templates and desired question types. An example file is provided (synthetic.default.json). For correct forms, check question_to_description in src/synthetic/preprocess.py. For question IDs, check keys in question_type_to_id

Dataset will be placed to data/processed_synthetic/X.

Generating synthetic "question-types" dataset

To generate the synthetic "question types" dataset, place the downloaded folders to data/figureqa (check the README there), and run

python3 src/synthetic/preprocess-question-types.py data/fiqureqa/X

Subject replacement (--replace-subjects), unrolling (--unroll) and synthetic config flag (--synthetic-config) can be provided, as for generating the normal synthetic dataset.

Generating natural-language dataset

To generate the natural-language dataset, place the downloaded folders to data/charttotext (check the README there), and run

python3 src/natural/preprocess.py

Dataset will be placed to data/processed_natural.