README.md

metadata

license: mit
language:
  - fa
multilinguality:
  - monolingual
size_categories:
  - 30k<n<50k
task_categories:
  - question-answering
  - conditional-text-generation
  - text-generation
task_ids:
  - conversational-question-answering
  - question-answering
pretty_name: SynTranFa

SynTran-fa

Syntactic Transformed Version of Farsi QA datasets to make fluent responses from questions and short answers.

Table of Contents

• Table of Contents
• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage: Sharif-SLPL
• Repository: SynTran-fa
• Point of Contact: Sadra Sabouri
• Size of dataset files: 6.12 MB

Dataset Summary

Generating fluent responses has always been challenging for the question-answering task, especially in low-resource languages like Farsi. In recent years there were some efforts for enhancing the size of datasets in Farsi. Syntran-fa is a question-answering dataset that accumulates the former Farsi QA dataset's short answers and proposes a complete fluent answer for each pair of (question, short_answer).

This dataset contains nearly 50,000 indices of questions and answers. The dataset that has been used as our sources were as follows:

• QA_DATASET1
• QA_DATASET2
• ...

The main idea for this dataset comes from Fluent Response Generation for Conversational Question Answering 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 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).

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.

Languages

• Persian (fa)

Dataset Structure

Data Instances

[More Information Needed]

Data Fields

[More Information Needed]

Data Splits

[More Information Needed]

Dataset Creation

[More Information Needed]

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

[More Information Needed]

Citation Information

[More Information Needed]

Contributions

Thanks to @farhaaaaa for adding this dataset.