The new generated dataset for PARARULE. It is generated based on the closed-world assumption.
PARARULE Plus is a deep multi-step reasoning dataset over natural language. It can be seen as an improvement on the dataset of PARARULE (Peter Clark et al., 2020). The motivation is to generate deeper PARARULE training samples. We add more training samples for the case where the depth is greater than or equal to two to explore whether Transformer has reasoning ability. PARARULE Plus is a combination of two types of entities, animals and people, and corresponding relationships and attributes. From the depth of 2 to the depth of 5, we have around 100,000 samples in the depth of each layer, and there are nearly 400,000 samples in total.
We published the PARARULE Plus dataset to the paper Multi-Step Deductive Reasoning Over Natural Language: An Empirical Study on Out-of-Distribution Generalisation. The code and experiment in this paper can be found in that GitHub link. PARARULE-Plus has also been collected by LogiTorch which is a PyTorch-based library for logical reasoning on natural language, ReasoningNLP and Prompt4ReasoningPapers. We also tested our PARARULE Plus with ChatGPT and GPT-4 and both of them failed on the tasks. Our pull request has been merged to OpenAI/Evals. More examples can be found in this tweet link.
You can use Huggingdace/Datasets to load the PARARULE-Plus dataset from this link. We upload PARARULE-Plus, PARARULE-Plus-Depth-2, PARARULE-Plus-Depth-3, PARARULE-Plus-Depth-4, and PARARULE-Plus-Depth-5 to the Huggingface/Datasets. PARARULE-Plus includes the datasets from all four different depths and the other datasets only include the dataset corresponding to each depth. For example, PARARULE-Plus-Depth-2 only includes the datasets with 2 reasoning depths. Here is an example to load the PARARULE-Plus with all reasoning depths.
conda create -n pararule-plus python=3.8
conda activate pararule-plus
git clone https://github.com/Strong-AI-Lab/PARARULE-Plus.git
cd PARARULE-Plus
pip install -r requirements
You also need to install apex, you can following the steps if you are using the Linux system.
git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
from datasets import load_dataset
dataset = load_dataset("qbao775/PARARULE-Plus") ## You can use "qbao775/PARARULE-Plus-Depth-2", "qbao775/PARARULE-Plus-Depth-3", "qbao775/PARARULE-Plus-Depth-4", and "qbao775/PARARULE-Plus-Depth-5" to load datasets from different reasoning depths.
python run_glue_no_trainer.py \
--model_name_or_path bert-base-cased \
--task_name qbao775/PARARULE-Plus\
--max_length 256 \
--per_device_train_batch_size 32 \
--learning_rate 2e-5 \
--num_train_epochs 3 \
--output_dir /tmp/PARARULE-Plus/
Animal Entities ['the bald eagle', 'the tiger', 'the bear', 'the lion', 'the wolf', 'the crocodile', 'the dinosaur', 'the snake', 'the leopard']
Animal Entities-2 ['the cat', 'the dog', 'the mouse', 'the rabbit', 'the squirrel']
Animal Relationship ['is', 'likes', 'chases', 'needs', 'visits', 'attacks', 'sees']
Animal Attributes-1 ['kind', 'quiet', 'round', 'nice', 'smart', 'clever']
Animal Attributes-2 ['dull', 'rough', 'lazy', 'slow', 'sleepy', 'boring', 'tired', 'reckless']
Animal Attributes-3 ['furry', 'small', 'cute', 'lovely', 'beautiful', 'funny']
Animal Attributes-4 = ['big', 'strong', 'awful', 'fierce', 'heavy', 'horrible', 'powerful', 'angry']
| The Num of Animal Entities | The Num of Animal Relationships | The Num of Animal Attributes |
|---|---|---|
| 14 | 7 | 28 |
People Entities ['Anne', 'Alan', 'Bob', 'Charlie', 'Dave', 'Erin', 'Harry', 'Gary', 'Fiona']
People Relationship ['is']
People Attributes-1 ['big', 'strong', 'high', 'huge']
People Attributes-2 ['short', 'thin', 'small', 'little', 'tiny']
People Attributes-3 ['wealthy', 'smart', 'nice', 'quiet', 'kind', 'clever']
People Attributes-4 = ['poor', 'dull', 'rough', 'bad', 'sad']
| The Num of People Entities | The Num of People Relationships | The Num of People Attributes |
|---|---|---|
| 9 | 1 | 20 |
For each depth dataset, we have around 90,000 datasets to be used, much larger than the same depth in PARARULE.
| Dataset | Train | Dev | Test |
|---|---|---|---|
| Depth=2 | 89952 | 16204 | 2708 |
| Depth=3 | 90016 | 16154 | 2694 |
| Depth=4 | 90010 | 16150 | 2704 |
| Depth=5 | 90022 | 16150 | 2692 |
| Dataset | Train | Dev | Test |
|---|---|---|---|
| Depth=0 | 290435 | 41559 | 83119 |
| Depth=1 | 157440 | 22276 | 45067 |
| Depth=2 | 75131 | 10833 | 21496 |
| Depth=3 | 48010 | 6959 | 13741 |
| Depth=4 | 9443 | 1334 | 2691 |
| Depth=5 | 7325 | 1038 | 2086 |
An example with the non-negation rules for Depth=2 means the question needed to be derived by two rules.
An example with the negation rules for Depth=2 means the question needed to be derived by two rules.
An example with the non-negation rules for Depth=3 means the question needed to be derived by three rules.
An example with the negation rules for Depth=3 means the question needed to be derived by three rules.
An example with the non-negation rules for Depth=4 means the question needed to be derived by four rules.
An example with the negation rules for Depth=4 means the question needed to be derived by four rules.
An example with the non-negation rules for Depth=5 means the question needed to be derived by five rules.
An example with the negation rules for Depth=5 means the question needed to be derived by five rules.
The QCat=0_not_notTrue means the question is generated from one negation rule and another negation rule and a positive rule and the label is true.
The QCat=0_0_not_notTrue means the question is generated from one negation rule and another negation rule and a positive rule and the label is false.
The QCat=0_true_trueNot means the question is generated from one positive rule and another positive rule and a negation rule and the label is true.
The QCat=0_0_true_trueNot means the question is generated from one positive rule and another positive rule and a negation rule and the label is false.
The QCat=0_not_notTrue_true means the question is generated from one negation rule and another negation rule and a positive rule and a positive rule and the label is true.
The QCat=0_0_not_notTrue_true means the question is generated from one negation rule and another negation rule and a positive rule and a positive rule and the label is false.
The QCat=0_true_trueNot_true means the question is generated from one positive rule and another positive rule and a negation rule and and a positive rule and the label is true.
The QCat=0_0_true_trueNot_true means the question is generated from one positive rule and another positive rule and a negation rule and a positive rule and the label is false.
The QCat=0_not_notTrue_true_true means the question is generated from one negation rule and another negation rule and a positive rule and two more positive rules and the label is true.
The QCat=0_0_not_notTrue_true_true means the question is generated from one negation rule and another negation rule and a positive rule and two more positive rules and the label is false.
The QCat=0_true_trueNot_true_true means the question is generated from one positive rule and another positive rule and a negation rule and two more positive rules and the label is true.
The QCat=0_0_true_trueNot_true_true means the question is generated from one positive rule and another positive rule and a negation rule and two more positive rules and the label is false.
The QCat=0_not_notTrue_true_true_true means the question is generated from one negation rule and another negation rule and a positive rule and three more positive rules and the label is true.
The QCat=0_0_not_notTrue_true_true_true means the question is generated from one negation rule and another negation rule and a positive rule and three more positive rules and the label is false.
The QCat=0_true_trueNot_true_true_true means the question is generated from one positive rule and another positive rule and a negation rule and three more positive rules and the label is true.
The QCat=0_0_true_trueNot_true_true_true means the question is generated from one positive rule and another positive rule and a negation rule and three more positive rules and the label is false.
new_data_generation_NegationRule-D2.py- The question needed to be derived by two rules, part of them are the negation rules.new_data_generation_NegationRule-animal-D2.py- The question with animal entities needed to be derived by two rules includes the negation rules.new_data_generation_NonNegationRule-D2.py- The question needed to be derived by two rules, all of them are the non-negation rules.new_data_generation_NonNegationRule-animal-D2.py- The question with animal entities needed to be derived by two rules includes the non-negation rules.
new_data_generation_NegationRule-D3.py- The question needed to be derived by three rules, part of them are the negation rules.new_data_generation_NegationRule-animal-D3.py- The question with animal entities needed to be derived by three rules includes the negation rules.new_data_generation_NonNegationRule-D3.py- The question needed to be derived by three rules, all of them are the non-negation rules.new_data_generation_NonNegationRule-animal-D3.py- The question with animal entities needed to be derived by three rules includes the non-negation rules.
new_data_generation_NegationRule-D4.py- The question needed to be derived by four rules, part of them are the negation rules.new_data_generation_NegationRule-animal-D4.py- The question with animal entities needed to be derived by four rules includes the negation rules.new_data_generation_NonNegationRule-D4.py- The question needed to be derived by four rules, all of them are the non-negation rules.new_data_generation_NonNegationRule-animal-D4.py- The question with animal entities needed to be derived by four rules includes the non-negation rules.
new_data_generation_NegationRule-D5.py- The question needed to be derived by five rules, part of them are the negation rules.new_data_generation_NegationRule-animal-D5.py- The question with animal entities needed to be derived by five rules includes the negation rules.new_data_generation_NonNegationRule-D5.py- The question needed to be derived by five rules, all of them are the non-negation rules.new_data_generation_NonNegationRule-animal-D5.py- The question with animal entities needed to be derived by five rules includes the non-negation rules.
shuffle_data.py - The generated data is shuffled by this scripts.
@inproceedings{bao2022multi,
title={Multi-Step Deductive Reasoning Over Natural Language: An Empirical Study on Out-of-Distribution Generalisation},
author={Qiming Bao and Alex Yuxuan Peng and Tim Hartill and Neset Tan and Zhenyun Deng and Michael Witbrock and Jiamou Liu},
booktitle={Proceedings of the 16th International Workshop on Neural-Symbolic Learning and Reasoning as part of the 2nd International Joint Conference on Learning \& Reasoning (IJCLR 2022)},
pages={202-217},
month=sep,
year={2022},
address={Cumberland Lodge, Windsor Great Park, United Kingdom},
}
The PARARULE dataset is from that paper. Transformers as Soft Reasoners over Language.
The online demo for PARARULE. https://rule-reasoning.apps.allenai.org/
PARARULE dataset https://allenai.org/data/ruletaker
Explaining Answers with Entailment Trees https://arxiv.org/pdf/2104.08661.pdf
ProofWriter: Generating Implications, Proofs, and Abductive Statements over Natural Language https://arxiv.org/pdf/2012.13048.pdf
PRover: Proof Generation for Interpretable Reasoning over Rules https://arxiv.org/abs/2010.02830
Neural Unification for Logic Reasoning over Natural Language https://www.researchgate.net/profile/Gabriele-Picco-2/publication/354653211_Neural_Unif[…]-Unification-for-Logic-Reasoning-over-Natural-Language.pdf
Measuring Systematic Generalization in Neural Proof Generation with Transformers https://arxiv.org/pdf/2009.14786.pdf
Investigating the Limitations of the Transformers with Simple Arithmetic Tasks http://arxiv.org/abs/2102.13019v1
NATURALPROOFS: Mathematical Theorem Proving in Natural Language https://arxiv.org/pdf/2104.01112.pdf
Graph-to-Tree Learning for Solving Math Word Problems https://www.aclweb.org/anthology/2020.acl-main.362.pdf
Natural Language Premise Selection: Finding Supporting Statements for Mathematical Text https://arxiv.org/pdf/2004.14959.pdf
Learning To Prove From Synthetic Theorems https://arxiv.org/pdf/2006.11259.pdf
Are Pretrained Language Models Symbolic Reasoners Over Knowledge? https://arxiv.org/pdf/2006.10413.pdf
Facts as Experts: Adaptable and Interpretable Neural Memory over Symbolic Knowledge https://arxiv.org/pdf/2007.00849.pdf
Compressive Transformers for Long-Range Sequence Modelling https://arxiv.org/abs/1911.05507
HOTPOTQA: A Dataset for Diverse, Explainable Multi-hop Question Answering https://www.aclweb.org/anthology/D18-1259.pdf
Applying the Closed World Assumption to SUMO-based FOL Ontologies for Effective Commonsense Reasoning http://ecai2020.eu/papers/1076_paper.pdf
Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge https://arxiv.org/pdf/2006.06609.pdf