SituatedGen: Incorporating Geographical and Temporal Contexts into Generative Commonsense Reasoning

This repository contains the data and code for the baseline described in the following paper:

SituatedGen: Incorporating Geographical and Temporal Contexts into Generative Commonsense Reasoning
Yunxiang Zhang, Xiaojun Wan
NeurIPS 2023 Datasets and Benchmarks Track

@inproceedings{zhang2023situatedgen,
  title={SituatedGen: Incorporating Geographical and Temporal Contexts into Generative Commonsense Reasoning},
  author={Zhang, Yunxiang and Wan, Xiaojun},
  booktitle={Proceedings of the Neural Information Processing Systems Track on
            Datasets and Benchmarks, NeurIPS Datasets and Benchmarks 2023, New Orleans, LA, United States, December 10-16, 
            2023},
}

Datasets

SituatedGen data files are located under data/.

• train.jsonl contains 5,641 training examples.
• dev.jsonl contains 1,407 development examples.
• test.jsonl contains 1,220 test examples.

The data files are formatted as jsonlines. Here is a single example:

{
      "keywords": ["approximately 365 days", "axis", "every 24 hours", "sun", "Earth", "Earth"],
      "statement": "Earth revolves around the sun approximately 365 days. Earth rotates on its axis once every 24 hours.",
      "ids": ["arc::Easy::Test::101", "arc::Easy::Test::110"],
      "keywords_pos": [0, 1, 1, 0, 1, 0],
      "statements": ["Earth revolves around the sun approximately 365 days.", "Earth rotates on its axis once every 24 hours."]
}

Field	Description
keywords	A list of input keywords
statement	The target output, which is a string concatenation of two sentences containing these keywords
ids	the origins of the two sentences (from which (train/dev/test) split of which source datasets/corpora) represented in the format of "{src_dataset}::{split}::{id}"
keywords_pos	At which sentence should the keyword appear (0 for the first sentence in "statements" field, 1 for the second)
statements	A list of the two sentences in the "statement" field

Baselines

To train models (bart/t5/flan-t5), run the following command after you install the dependencies with pip install -r requirements.txt (Python=3.7)

bash scripts/keyword2text/train_plm.sh ${GPU_NO} ${MODEL_NAME}

To evaluate the predictions, run

bash scripts/keyword2text/pred_plm.sh ${GPU_NO} ${MODEL_NAME}