File size: 2,607 Bytes
7061059 b34b9ac 7061059 b34b9ac 7061059 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 |
---
language:
- en
- te
license: apache-2.0
task_categories:
- question-answering
- text-retrieval
- text-generation
pretty_name: Telugu QA Paraphrases
size_categories:
- 1K<n<10K
---
# Telugu QA Paraphrases
A synthetic multilingual query-rewriting dataset for evaluating retrieval robustness under Telugu-English code mixing.
## Dataset Description
This dataset extends an existing Telugu QA dataset by generating multiple query variants with increasing levels of Telugu-English code mixing.
Each example contains:
- `question` : Original English question
- `answer` : Ground-truth answer
- `level_0` : English paraphrase
- `level_1` : Light Telugu-English code mixing
- `level_2` : Moderate Telugu-English code mixing
- `level_3` : Heavy Telugu-English code mixing
- `level_4` : Romanized Telugu
- `level_5` : Telugu script
## Motivation
Multilingual embedding models are often evaluated on pure English or fully translated queries.
Real-world users frequently write:
- Telugu-English code mixed text
- Romanized Telugu
- Mixed-script queries
This dataset enables evaluation of retrieval robustness across progressively increasing code-mixing levels.
## Example
```json
{
"question": "How do I apply for a passport in India?",
"answer": "...",
"level_0": "How can I apply for an Indian passport?",
"level_1": "భారతదేశంలో పాస్పోర్ట్ ఎలా అప్లై చెయ్యాలి?",
"level_2": "India-lo passport apply process enti?",
"level_3": "భారత్ పాస్పోర్ట్ అప్లికేషన్ ఎలా చేయాలి?",
"level_4": "India passport apply ela?",
"level_5": "భారతదేశ పాస్పోర్ట్ కోసం ఎలా అప్లై చేయాలి?"
}
```
## Intended Uses
- Multilingual retrieval
- Dense retrieval evaluation
- RAG benchmarking
- Code-mixed query understanding
- Embedding model robustness analysis
## Source Dataset
This dataset was generated from:
https://huggingface.co/datasets/gurumurthy3/Legal-FAQ
Please cite and credit the original dataset creators.
## Generation
Query variants were generated using Sarvam-M and subsequently filtered for semantic preservation.
## Limitations
The generated paraphrases may contain:
- Imperfect transliteration
- Script mixing
- Translation artifacts
- Semantic drift in a small number of examples
Manual validation is recommended for benchmark creation.
## Citation
```bibtex
@dataset{telugu_qa_paraphrases,
title={Telugu QA Paraphrases},
author={Suhas Koheda},
year={2026}
}
```
|