Create README.md

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README.md

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+---
+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
+}
+```
+