README.md

---
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])