Code and data will be finalized closer to the conference.
You can download the Webis-CausalQA-22 corpus. To recreate the ELI5 part, check instructions bellow.
The 10 datasets used to construct Webis-CausalQA-22 corpus:
| Dataset | Source | License | License type |
|---|---|---|---|
PAQ |
https://github.com/facebookresearch/PAQ | https://github.com/facebookresearch/PAQ#data-license | CC BY-SA 3.0 |
GooAQ |
https://github.com/allenai/gooaq/ | https://github.com/allenai/gooaq/blob/main/LICENSE | Apache License V. 2.0 |
MS MARCO |
https://microsoft.github.io/msmarco/ | same as source | Own Terms |
Natural Questions |
https://ai.google.com/research/NaturalQuestions/download | same as source | CC BY-SA 3.0 |
ELI5 |
https://github.com/facebookresearch/ELI5 or https://huggingface.co/datasets/eli5 (used) | same as source | Hosting not allowed |
SearchQA |
https://github.com/nyu-dl/dl4ir-searchQA | same as source | No information |
SQuAD 2.0 |
https://rajpurkar.github.io/SQuAD-explorer/ | same as source | CC BY-SA 4.0 |
NewsQA |
https://github.com/Maluuba/newsqa | same as source | Own Terms |
HotpotQA |
https://hotpotqa.github.io/ | same as source | CC BY-SA 4.0 |
TriviaQA |
https://nlp.cs.washington.edu/triviaqa/index.html | same as source | No information |
ELI5 is also available in Hugging Face https://huggingface.co/datasets/eli5 that contains a script for downloading the data. This blog post provides a guide of how to download the data as well: https://yjernite.github.io/lfqa.html (was used).
Example to obtain the ELI5 data
pip install nlp
import nlp
eli5 = nlp.load_dataset('eli5')
train_set = eli5['train_eli5']
val_set = eli5['validation_eli5']
Use the regex rules to identify causal questions.
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