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--- |
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languages: |
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- en |
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licenses: |
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- unknown |
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multilinguality: |
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- monolingual |
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size_categories: |
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- 10K<n<100K |
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--- |
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# Dataset Card for CLUTRR |
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## Table of Contents |
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## Dataset Description |
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### Dataset Summary |
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**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. |
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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. |
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### Dataset Task |
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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. |
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## Dataset Structure |
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We show detailed information for all 17 configurations of the dataset. |
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### configurations: |
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**id**: a unique series of characters and numbers that identify each instance <br> |
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**story**: one semi-synthetic story involving hypothetical families<br> |
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**query**: the target query/relation which contains two names, where the goal is to classify the relation that holds between these two entities<br> |
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**text\_query**: <br> |
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**target**: correct relation for the query <br> |
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(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> |
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**text\_target**: <br> |
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**clean\_story**: <br> |
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**proof\_state**: the logical rule of the kinship generation <br> |
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**f\_comb**: the kinships of the query followed by the logical rule<br> |
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**task\_name**: the task of the sub-dataset in a form of "task_[num1].[num2]"<br> |
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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> |
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The second number [num2] directly indicates the length of clauses for the task target.<br> |
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*for example:*<br> |
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*task_1.2 -- task requiring clauses of length 2 without adding noise facts*<br> |
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*task_2.3 -- task requiring clauses of length 3 with Irrelevant noise facts added in the story*<br> |
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**story\_edges**: all the edges in the kinship graph<br> |
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**edge\_types**: similar to the f\_comb, another form of the query's kinships followed by the logical rule <br> |
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**query\_edge**: the corresponding edge of the target query in the kinship graph<br> |
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**genders**: genders of names appeared in the story<br> |
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**syn\_story**: <br> |
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**node\_mapping**: <br> |
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**task\_split**: train,test <br> |
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*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 |
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### Data Instances |
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An example of 'train'in Task 1.2 looks as follows. |
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``` |
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{ |
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"id": b2b9752f-d7fa-46a9-83ae-d474184c35b6, |
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"story": "[Lillian] and her daughter [April] went to visit [Lillian]'s mother [Ashley] last Sunday.", |
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"query": ('April', 'Ashley'), |
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"text_query": , |
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"target": "grandmother", |
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"text_target": ['[Ashley] has a granddaughter called [April] who is her favourite.'], |
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"clean_story": [Lillian] and her daughter [April] went to visit [Lillian]'s mother [Ashley] last Sunday., |
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"proof_state": [{('April', 'grandmother', 'Ashley'): [('April', 'mother', 'Lillian'), ('Lillian', 'mother', 'Ashley')]}], |
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"f_comb": "mother-mother", |
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"task_name": "task_1.2", |
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"story_edges": [(0, 1), (1, 2)], |
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"edge_types": ['mother', 'mother'], |
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"query_edge": (0, 2), |
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"genders": "April:female,Lillian:female,Ashley:female"; |
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"syn_story": , |
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"node_mapping": {7: 0, 2: 1, 1: 2}, |
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"task_split": trian |
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} |
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``` |
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### Data Splits |
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| task_split | train |test | |
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| :---: | :-: | :-: | |
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| task_1.2 | 15227 | 114 | |
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| task_1.3 | 15050 | 621 | |
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| task_1.4 | 5024 | 621 | |
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| task_1.5 | 0 | 359 | |
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| task_1.6 | 0 | 212 | |
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| task_1.7 | 0 | 299 | |
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| task_1.8 | 0 | 285 | |
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| task_1.9 | 0 | 243 | |
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| task_1.10 | 0 | 241 | |
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| task_2.2 | 5107 | 38 | |
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| task_2.3 | 5047 | 408 | |
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| task_3.2 | 5104 | 38 | |
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| task_3.3 | 4995 | 407 | |
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| task_4.2 | 5096 | 38 | |
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| task_4.3 | 5004 | 407 | |
