Update README.md

README.md

@@ -55,7 +55,7 @@ Generating fluent responses has always been challenging for the question-answeri
 
 This dataset contains nearly 50,000 indices of questions and answers. The dataset that has been used as our sources are in [Source Data section](#source-data).
 
-The main idea for this dataset comes from [Fluent Response Generation for Conversational Question Answering](https://aclanthology.org/2020.acl-main.19.pdf) where they used a "parser + syntactic rules" module to make different fluent answers from a pair of question and a short answer using a parser and some syntactic rules. In this project, we used [stanza](https://stanfordnlp.github.io/stanza/) as our parser to parse the question and generate a response according to it using the short (1-2 word) answers. One can continue this project by generating different permutations of the sentence's parts (and thus providing more than one sentence for an answer) or training a seq2seq model which does what we do with our rule-based system (by defining a new text-to-text task).
+The main idea for this dataset comes from [Fluent Response Generation for Conversational Question Answering](https://aclanthology.org/2020.acl-main.19.pdf) where they used a "parser + syntactic rules" module to make different fluent answers from a pair of question and a short answer using a parser and some syntactic rules. In this project, we used [stanza](https://stanfordnlp.github.io/stanza/) as our parser to parse the question and generate a response according to it using the short (sentences without verbs - up to ~4 words) answers. One can continue this project by generating different permutations of the sentence's parts (and thus providing more than one sentence for an answer) or training a seq2seq model which does what we do with our rule-based system (by defining a new text-to-text task).
 
 ### Supported Tasks and Leaderboards
 
@@ -90,7 +90,7 @@ Currently, the dataset just provided the `train` split. There would be a `test`
 
 ## Dataset Creation
 
-We extract all short answer (1-2 words as answer) entries of all open source QA datasets in Farsi and used some rules featuring the question parse tree to make long (fluent) answers.
+We extract all short answer (sentences without verbs - up to ~4 words) entries of all open source QA datasets in Farsi and used some rules featuring the question parse tree to make long (fluent) answers.
 
 ### Source Data
 The source datasets that we used are as follows: