Datasets:
Tasks:
Translation
Modalities:
Text
Formats:
json
Languages:
Chhintange
Size:
1K - 10K
ArXiv:
License:
File size: 2,068 Bytes
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license: cc-by-nc-sa-4.0
language:
- ctn
pretty_name: LingReason Chintang Data
task_categories:
- translation
tags:
- low-resource-machine-translation
- linguistic-reasoning
- universal-dependencies
- chintang
configs:
- config_name: default
data_files:
- split: train
path: data/ctn_train.json
- split: validation
path: data/ctn_eval.json
- split: test
path: data/ctn_test.json
- split: train_no_thinking
path: data/ctn_no_thinking_train.json
- split: validation_no_thinking
path: data/ctn_no_thinking_eval.json
- split: test_icl
path: data/ctn_test_icl.json
---
## Dataset Description
This dataset contains Chintang data for the LingReason project, which is generated from using the code released in the [LingReason GitHub repository](https://github.com/OLAResearch/LingReason).
This dataset accompanies the paper [Reasoning over Grammar: Can Synthetic Linguistic Reasoning Traces Enhance Low-Resource Machine Translation?](https://arxiv.org/abs/2606.03782).
## Data Splits
| Split | File | Examples | Description |
|---|---:|---:|---|
| `test_icl` | `ctn_test_icl.json` | 344 | Test set with linguistic reasoning guides (with placeholders) in the prompt, used for in-context learning experiment. |
| `train` | `ctn_train.json` | 1,831 | SFT/RFT train set with completed linguistic reasoning traces in the <think> block. |
| `validation` | `ctn_eval.json` | 114 | Evaluation/Validation set with completed linguistic reasoning traces in the <think> block. |
| `train_no_thinking` | `ctn_no_thinking_train.json` | 1,831 | SFT Train set without linguistic reasoning traces. |
| `validation_no_thinking` | `ctn_no_thinking_eval.json` | 114 | Evaluation/Validation set without linguistic reasoning traces. |
| `test` | `ctn_test.json` | 344 | Test set with completed linguistic reasoning traces in the <think> block, used for ICL baseline as well as SFT and RFT experiments. |
## Usage
```python
from datasets import load_dataset
dataset = load_dataset("OLAResearchX/LingReason")
print(dataset)
print(dataset["train"][0]) |