---
license: apache-2.0
---

# YUE-PUB


This repo contains the data for our ****AACL-IJCNLP 2026**** paper ****YUE-PUB: A Cantonese Pragmatic Understanding Benchmark (https://aclanthology.org/2026.findings-aacl.1/)****.

The benchmark consists of **14** tasks collected from **7** existing pragmatic datasets, covering different forms of pragmatic reasoning, including response classification, implicature recovery, figurative language understanding, presupposition inference, and reference resolution.

## Task Description

The benchmark consists of **14** pragmatic understanding tasks, which are grouped into three major pragmatic phenomena.

| Task ID | Phenomenon | Description | Source |
|---------|------------|-------------|--------|
| Task 1–2 | Implicature | Response Classification | CIRCA |
| Task 3 | Implicature | Response Classification | CIRCA |
| Task 4 | Implicature | Implicature Recovery | GRICE |
| Task 5–6 | Implicature | Agreement / Sarcasm Detection | FigQA |
| Task 7 | Implicature | Figurative Language Understanding | FLUTE |
| Task 8–9 | Implicature | Figurative Language Understanding | FLUTE |
| Task 10 | Implicature | Implicature Natural Language Inference (NLI) | IMPPRES |
| Task 11 | Presupposition | Presupposition Natural Language Inference (NLI) | IMPPRES |
| Task 12 | Presupposition | Presupposition Question Answering | DailyDialog |
| Task 13 | Reference | Deictic Question Answering | GRICE |
| Task 14 | Reference | Reference Resolution via Metonymy | Metonymy |

## Data Description

This is a dataset suitable for the **Cantonese pragmatic understanding** task with **multiple-choice question answering**.

All datasets are stored in the `dataset/` folder.

- The benchmark covers **3** major pragmatic phenomena.

```python
phenomena = [
    "implicature",
    "presupposition",
    "reference"
]
```

- The benchmark consists of **14** tasks collected from **7** existing pragmatic datasets.

```python
sources = [
    "CIRCA",
    "GRICE",
    "FigQA",
    "FLUTE",
    "IMPPRES",
    "DailyDialog",
    "Metonymy"
]
```


- Each instance contains the dialogue context, question, answer options, ground-truth answer, and the corresponding speech recording. An example is shown below.

```json
{
    "id": "0",
    "context": "背景：X同Y係星期五放工時間差唔多嘅同事。",
    "question": "問：想唔想我順便車你返屋企？",
    "response": "答：我想我自己車我自己返屋企。",
    "options": [
        "直接回答",
        "間接回答"
    ],
    "answer": "直接回答"
}
```


## Citation

```
@inproceedings{wu-etal-2026-yue,
    title = "{YUE}-{PUB}: A {C}antonese Pragmatic Understanding Benchmark",
    author = "Wu, Chengyan  and
      Gong, Ziwei  and
      Chen, Jinzhi  and
      Wen, Yajie and
      Su, Mengmeng  and
      Xue, Yun  and
      Ma, Bolei",
    editor = "Zhang, Yue  and
      Xu, Ruifeng  and
      Purwarianti, Ayu  and
      Rong, Tong  and
      Suzuki, Jun",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {IJCNLP}-{AACL} 2026",
    month = nov,
    year = "2026",
    address = "Hengqin, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.findings-aacl.1/",
    pages = "1--22",
    ISBN = "979-8-89176-444-6",
}
```