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--- |
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language: |
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- en |
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license: |
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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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Join the CLUTRR community in https://www.cs.mcgill.ca/~ksinha4/clutrr/ |
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## Dataset Structure |
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We show detailed information for all 14 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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**target**: indicator for the correct relation for the query <br> |
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**target_text**: text for the correct relation for the query <br> |
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the indicator follows the rule as follows: <br> "aunt": 0, "son-in-law": 1, "grandfather": 2, "brother": 3, |
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"sister": 4, |
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"father": 5, |
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"mother": 6, |
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"grandmother": 7, |
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"uncle": 8, |
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"daughter-in-law": 9, |
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"grandson": 10, |
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"granddaughter": 11, |
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"father-in-law": 12, |
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"mother-in-law": 13, |
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"nephew": 14, |
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"son": 15, |
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"daughter": 16, |
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"niece": 17, |
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"husband": 18, |
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"wife": 19, |
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"sister-in-law": 20 <br> |
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**clean\_story**: the story without noise factors<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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**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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"target": 7, |
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"target_text": "grandmother", |
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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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"task_split": trian |
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} |
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``` |
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### Data Splits |
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#### Data Split Name |
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(corresponding with the name used in the paper) |
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| task_split | split name in paper | train &validation task |test task | |
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| :---: | :---: | :-: | :-: | |
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| gen_train23_test2to10 | data_089907f8 | 1.2, 1.3 | 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 1.10 | |
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| gen_train234_test2to10 | data_db9b8f04 | 1.2, 1.3, 1.4| 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 1.10 | |
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| rob_train_clean_23_test_all_23 | data_7c5b0e70 | 1.2,1.3 | 1.2, 1.3, 2.3, 3.3, 4.3 | |
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| rob_train_sup_23_test_all_23 | data_06b8f2a1 | 2.2, 2.3 | 2.2, 2.3, 1.3, 3.3, 4.3 | |
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| rob_train_irr_23_test_all_23 | data_523348e6 | 3.2, 3.3 | 3.2, 3.3, 1.3, 2.3, 4.3 | |
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| rob_train_disc_23_test_all_23 | data_d83ecc3e | 4.2, 4.3 | 4.2, 4.3, 1.3, 2.3, 3.3 | |
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#### Data Split Summary |
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Number of Instances in each split |
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| task_split | train | validation | test | |
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| :-: | :---: | :---: | :---: | |
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| gen_train23_test2to10 | 9074 | 2020 | 1146 | |
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| gen_train234_test2to10 | 12064 | 3019 | 1048 | |
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| rob_train_clean_23_test_all_23 | 8098 | 2026 | 447 | |
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| rob_train_disc_23_test_all_23 | 8080 | 2020 | 445 | |
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| rob_train_irr_23_test_all_23 | 8079 | 2020 | 444 | |
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| rob_train_sup_23_test_all_23 | 8123 | 2031 | 447 | |
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## Citation Information |
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``` |
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@article{sinha2019clutrr, |
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Author = {Koustuv Sinha and Shagun Sodhani and Jin Dong and Joelle Pineau and William L. Hamilton}, |
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Title = {CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text}, |
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Year = {2019}, |
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journal = {Empirical Methods of Natural Language Processing (EMNLP)}, |
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arxiv = {1908.06177} |
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} |
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``` |