Update README.md

README.md

@@ -24,17 +24,15 @@ CLUTRR contains a large set of semi-synthetic stories involving hypothetical fam
 
 Join the CLUTRR community in  https://www.cs.mcgill.ca/~ksinha4/clutrr/ 
 ## Dataset Structure
-We show detailed information for all 17 configurations of the dataset.
+We show detailed information for all 13 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>
+**clean\_story**: the story without noise factors<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> 
@@ -47,8 +45,6 @@ The second number [num2] directly indicates the length of clauses for the task t
 **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
@@ -61,9 +57,7 @@ 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",
@@ -71,9 +65,7 @@ An example of 'train'in Task 1.2 looks as follows.
   "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},
+  "genders": "April:female,Lillian:female,Ashley:female",
   "task_split": trian
 }
 ```