license: mit
LogiQA2.0
Logiqa2.0 dataset - logical reasoning in MRC and NLI tasks
This is the official repository for the LogiQA 2.0 datasets in our paper LogiQA2.0 - An Improved Dataset for Logic Reasoning in Question Answering and Textual Inference
How to cite
@ARTICLE{10174688,
author={Liu, Hanmeng and Liu, Jian and Cui, Leyang and Teng, Zhiyang and Duan, Nan and Zhou, Ming and Zhang, Yue},
journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
title={LogiQA 2.0—An Improved Dataset for Logical Reasoning in Natural Language Understanding},
year={2023},
volume={31},
number={},
pages={2947-2962},
doi={10.1109/TASLP.2023.3293046}}
About
This is version 2 of the LogiQA dataset, first released as a multi-choice reading comprehension dataset by our previous paper LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning.
The dataset is collected from the Chinese Civil Service Entrance Examination. The dataset is both in Chinese and English (by translation). you can download the version 1 of the LogiQA dataset from here.
To construct the LogiQA2.0 dataset, we:
- Collect more newly released exam questions and practice questions. There are about 20 provinces in China that hold the exam annually. The exam materials are publicly available on the Internet after the exams. Besides, practice questions are provided by various sources.
- Hire professional translators to re-translate the dataset from Chinese to English; verify the labels and annotations with human experts. This program is conducted by Speechocean, a data annotation service provider. The project is accomplished with the help of Microsoft Research Asia.
- Introduce a new NLI task to the dataset. The NLI version of the dataset is converted from the MRC version of the dataset, following previous work such as Transforming Question Answering Datasets into Natural Language Inference Datasets.
Datasets
MRC
The MRC part of the LogiQA2.0 dataset can be found in the /MRC/ folder.
train.txt: train split of the dataset in json lines.
dev.txt: dev split of the dataset in json lines.
test.txt: test split of the dataset in json lines.
train_zh.txt: train split of the Chinese version of dataset in json lines.
dev_zh.txt: dev split of the Chinese version of dataset in json lines.
test_zh.txt: test split of the Chinese version of dataset in json lines.
train_fol.zip is the training data with AMR and FOL annotations. The file is too big so we compressed it.
dev_fol.jsonl is the dev data with AMR and FOL annotations.
test_fol.jsonl is the test data with AMR and FOL annotations.
An example:
{"id": 10471, "answer": 0, "text": "The medieval Arabs had many manuscripts of ancient Greek. When needed, they translate them into Arabic. Medieval Arab philosophers were very interested in Aristotle's Theory of Poetry, which was obviously not shared by Arab poets, because a poet interested in it must want to read Homer's poems. Aristotle himself often quotes Homer's poems. However, Homer's poems were not translated into Arabic until modern times.", "question": "Which of the following options, if true, strongly supports the above argument?", "options": ["Some medieval Arab translators have manuscripts of Homer poems in ancient Greek.", "Aristotle's Theory of Poetry is often quoted and commented by modern Arab poets.", "In Aristotle's Theory of Poetry, most of the content is related to drama, and medieval Arabs also wrote plays and performed them.", "A series of medieval Arab stories, such as Arab Night, are very similar to some parts of Homer's epic."], "type": {"Sufficient Conditional Reasoning": true, "Necessry Condtional Reasoning": true, "Conjunctive Reasoning": true}}
An example of the Chinese dataset:
{"id": 8018, "answer": 0, "text": "常春藤通常指美国东部的八所大学。常春藤一词一直以来是美国名校的代名词，这八所大学不仅历史悠久，治学严谨，而且教学质量极高。这些学校的毕业生大多成为社会精英，他们中的多数人年薪超过20万美元，有很多政界领袖来自常春藤，更有为数众多的科学家毕业于长春藤。", "question": "根据以上条件，下面那个选项一定为真:", "options": ["A.有些社会精英年薪超过20万美金", "B.有些政界领袖年薪不足20万美元", "C.有些科学家年薪超过20万美元", "D.有些政界领袖是社会精英"]}
NLI
The NLI part of LogiQA2.0 dataset can be found in the /NLI/ folder.
train.txt: train split of the dataset in json lines
dev.txt: dev split of the dataset in json lines
test.txt: test split of the dataset in json lines
An example:
{"label": "not entailed", "major_premise": ["Among the employees of a software company, there are three Cantonese, one Beijinger and three northerners"], "minor_premise": " Four are only responsible for software development and two are only responsible for product sales", "conclusion": "There may be at least 7 people and at most 12 people."}
Annotations
The translation and annotation work is outsourced to Speechocean, and the project fund is provided by Microsoft Research Asia
Translation
| Final Report | |
|---|---|
| provider | Speechocean |
| Project Duration | 2021/10/20-2021/12/3 |
| Actual Working Hour | 667 hours |
| Cost | 45000 RMB |
Translation style/method:
Maintain a unified style, and the translated English questions need to inherit the logic of the original questions.
The pronoun in the question needs to be unique, the translation needs to be unique and consistent without ambiguity;
The translated English conforms to the form of a proper question, that is, it is a clear question from the perspective of the respondent;
Label consistency
The label credibility is manually verified after the translation is done to maintain the truthfulness of the original text. 3 workers run a consistency test on each example, if 2 or more workers give different answer compared to the original answer, the translation would be redone to guareentee the label is correct.
Additional annotations
The reasoning types of each question are assigned by a total of 5 workers, each of whom corresponds to one reasoning type. We describe reasoning types (which can be found in our paper) to the workers. The reasoning types of each question is a collection of 5 workers' decision.