---
languages:
- en
licenses:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
---

# Dataset Card for CLUTRR 

## Table of Contents

## Dataset Description
### Dataset Summary
**CLUTRR** (**C**ompositional **L**anguage **U**nderstanding and **T**ext-based **R**elational **R**easoning), a diagnostic benchmark suite, is first introduced in (https://arxiv.org/abs/1908.06177) to test the systematic generalization and inductive reasoning capabilities of NLU systems.  

The CLUTRR benchmark allows us to test a model’s ability for **systematic generalization** by testing on stories that contain unseen combinations of logical rules, and test for the various forms of **model robustness** by adding different kinds of superfluous noise facts to the stories.

### Dataset Task
CLUTRR contains a large set of semi-synthetic stories involving hypothetical families. The task is to infer the relationship between two family members, whose relationship is not explicitly mentioned in the given story.

## Dataset Structure
We show detailed information for all 17 configurations of the dataset.

### configurations:
**id**: a unique series of characters and numbers that identify each instance <br>
**story**: one semi-synthetic story involving hypothetical families<br>
**query**: the target query/relation which contains two names, where the goal is to classify the relation that holds between these two entities<br>
**text\_query**: <br>
**target**: correct relation for the query <br>
(the following kin-ship relations are used: son, father, husband, brother, grandson, grandfather, son-in-law, father-in-law, brother-in-law, uncle, nephew, daughter, mother, wife, sister, granddaughter, grandmother, daughter-in-law, mother-in-law, sister-in-law, aunt, niece.)<br>
**text\_target**: <br>
**clean\_story**: <br>
**proof\_state**: the logical rule of the kinship generation <br>
**f\_comb**: the kinships of the query followed by the logical rule<br>
**task\_name**: the task of the sub-dataset in a form of "task_[num1].[num2]"<br> 
The first number [num1] indicates the status of noise facts added in the story: 1- no noise facts; 2- Irrelevant facts*; 3- Supporting facts*; 4- Disconnected facts*.<br>
The second number [num2] directly indicates the length of clauses for the task target.<br>
*for example:*<br>
*task_1.2 -- task requiring clauses of length 2 without adding noise facts*<br> 
*task_2.3 -- task requiring clauses of length 3 with Irrelevant noise facts added in the story*<br> 
**story\_edges**: all the edges in the kinship graph<br>
**edge\_types**: similar to the f\_comb, another form of the query's kinships followed by the logical rule <br>
**query\_edge**: the corresponding edge of the target query in the kinship graph<br>
**genders**: genders of names appeared in the story<br>
**syn\_story**: <br>
**node\_mapping**: <br>
**task\_split**: train,test <br>

*Further explanation of Irrelevant facts, Supporting facts and Disconnected facts can be found in the 3.5 Robust Reasoning section in https://arxiv.org/abs/1908.06177

### Data Instances

An example of 'train'in Task 1.2 looks as follows.
```
{
  "id": b2b9752f-d7fa-46a9-83ae-d474184c35b6,
  "story": "[Lillian] and her daughter [April] went to visit [Lillian]'s mother [Ashley] last Sunday.",
  "query": ('April', 'Ashley'),
  "text_query": ,
  "target": "grandmother",
  "text_target": ['[Ashley] has a granddaughter called [April] who is her favourite.'],
  "clean_story": [Lillian] and her daughter [April] went to visit [Lillian]'s mother [Ashley] last Sunday.,
  "proof_state": [{('April', 'grandmother', 'Ashley'): [('April', 'mother', 'Lillian'), ('Lillian', 'mother', 'Ashley')]}],
  "f_comb": "mother-mother",
  "task_name": "task_1.2",
  "story_edges": [(0, 1), (1, 2)],
  "edge_types": ['mother', 'mother'],
  "query_edge": (0, 2),
  "genders": "April:female,Lillian:female,Ashley:female";
  "syn_story": ,
  "node_mapping": {7: 0, 2: 1, 1: 2},
  "task_split": trian
}
```
### Data Splits

| task_split | train |test |
| :---:   | :-: | :-: |
| task_1.2 | 15227 | 114 | 
| task_1.3 | 15050 | 621 |    
| task_1.4 | 5024 | 621 |   
| task_1.5 | 0 | 359 |  
| task_1.6 | 0 | 212 |
| task_1.7 | 0 | 299 |
| task_1.8 | 0 | 285 |
| task_1.9 | 0 | 243 |
| task_1.10 | 0 | 241 |
| task_2.2 | 5107 | 38 |
| task_2.3 | 5047 | 408 |
| task_3.2 | 5104 | 38 |
| task_3.3 | 4995 | 407 |
| task_4.2 | 5096 | 38 |
| task_4.3 | 5004 | 407 |