RuWorldTree is a QA dataset with multiple-choice elementary-level science questions, which evaluate the understanding of core science facts.

Motivation

The WorldTree dataset starts the triad of the Reasoning and Knowledge tasks. The data includes the corpus of factoid utterances of various kinds, complex factoid questions and a corresponding causal chain of facts from the corpus resulting in a correct answer.

The WorldTree design was originally proposed in (Jansen et al., 2018).

An example in English for illustration purposes:

```{ 'question': 'A bottle of water is placed in the freezer. What property of water will change when the water reaches the freezing point? (A) color (B) mass (C) state of matter (D) weight',

'answer': 'C',

'exam_name': 'MEA',

'school_grade': 5,

'knowledge_type': 'NO TYPE',

'perturbation': 'ru_worldtree',

'episode': [18, 10, 11]

}```

Data Fields

• text: a string containing the sentence text
• answer: a string with a candidate for the coreference resolution
• options: a list of all the possible candidates present in the text
• reference: a string containing an anaphor (a word or phrase that refers back to an earlier word or phrase)
• homonymia_type: a float corresponding to the type of the structure with syntactic homonymy
• label: an integer, either 0 or 1, indicating whether the homonymy is resolved correctly or not perturbation: a string containing the name of the perturbation applied to text. If no perturbation was applied, the dataset name is used
• episode: a list of episodes in which the instance is used. Only used for the train set

Data Splits

The dataset consists of a training set with labeled examples and a test set in two configurations:

• raw data: includes the original data with no additional sampling
• episodes: data is split into evaluation episodes and includes several perturbations of test for robustness evaluation

We use the same splits of data as in the original English version.

Test Perturbations

Each training episode in the dataset corresponds to seven test variations, including the original test data and six adversarial test sets, acquired through the modification of the original test through the following text perturbations:

• ButterFingers: randomly adds noise to data by mimicking spelling mistakes made by humans through character swaps based on their keyboard distance
• Emojify: replaces the input words with the corresponding emojis, preserving their original meaning
• EDAdelete: randomly deletes tokens in the text
• EDAswap: randomly swaps tokens in the text
• BackTranslation: generates variations of the context through back-translation (ru -> en -> ru)
• AddSent: replaces one or more choice options with a generated one