update : citation information added.

README.md

@@ -56,6 +56,9 @@ This dataset contains nearly 50,000 indices of questions and answers. The datase
 
 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).
 
+For more information check the technical preprint here:
+[SynTran-fa: Generating Comprehensive Answers for Farsi QA Pairs via Syntactic Transformation](https://www.preprints.org/manuscript/202410.1684/v1)
+
 ### Supported Tasks and Leaderboards
 
 This dataset can be used for the question-answering task, especially when you are going to generate fluent responses. You can train a seq2seq model with this dataset to generate fluent responses - as done by [Fluent Response Generation for Conversational Question Answering](https://aclanthology.org/2020.acl-main.19.pdf).
@@ -125,7 +128,15 @@ MIT
 
 ### Citation Information
 
-[More Information Needed]
+```bibtex
+@article{farsi2024syntran,
+  title={SynTran-fa: Generating Comprehensive Answers for Farsi QA Pairs via Syntactic Transformation},
+  author={Farsi, Farhan and Sabouri, Sadra and Kashfipour, Kian and Gooran, Soroush and Sameti, Hossein and Asgari, Ehsaneddin},
+  year={2024},
+  doi="10.20944/preprints202410.1684.v1",
+  publisher={Preprints}
+}
+```
 
 ### Contributions