---
language:
- en
license: cc-by-nc-sa-4.0
pretty_name: DiPlomat
dataset_info:
  config_name: PIR_first
  features:
  - name: text
    sequence: string
  - name: speaker
    sequence: string
  - name: correct_turn_number
    sequence: int64
  splits:
  - name: train
    num_bytes: 3442927
    num_examples: 3341
  - name: validation
    num_bytes: 161433
    num_examples: 168
  - name: test
    num_bytes: 687605
    num_examples: 668
  download_size: 0
  dataset_size: 4291965
---
# The Dataset Presented Here is NOT Ready, please download from the [website](https://diplomat-dataset.github.io)
# DiPlomat

<!-- Provide a quick summary of the dataset. -->

Pragmatic reasoning plays a pivotal role in deciphering implicit meanings that frequently arise in real-life 
conversations and is essential for the development of communicative social agents. 
In this paper, we introduce a novel challenge, **DiPlomat**, aiming at benchmarking machines’ capabilities 
on pragmatic reasoning and situated conversational understanding. 
Compared with previous works that treat different figurative expressions 
(e.g. metaphor, sarcasm) as individual tasks, **DiPlomat** provides a cohesive framework 
towards general pragmatic understanding.
## Dataset Details
The **DiPlomat** dataset owns 4,177 data and covers a vocabulary of 48,900 words. 
More than that, human-annotated answers reach an amount of 6,494, 
hold a vocabulary size of 20,000, and cover 5 types of reasoning. 
Along with the dataset, we propose two tasks:  
Pragmatic Identification and Reasoning (PIR) and Conversational Question Answering. Furthermore, we provide the
data that we use for zero-NLI in the [paper](https://arxiv.org/abs/2306.09030).


- **Language(s) (NLP):** [English]
- **License:** [CC BY-NC-SA (Attribution-NonCommercial-ShareAlike)](https://creativecommons.org/licenses/by-nc-sa/4.0/)

### Dataset Sources 

<!-- Provide the basic links for the dataset. -->

- **[Repository](https://github.com/diplomat-dataset/diplomat)** 
- **Paper:** [DiPlomat: A Dialogue Dataset for Situated Pragmatic Reasoning](https://arxiv.org/abs/2306.09030)


## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
We provide 5 fields. ``PIR_first_subtask_dataset``, ``PIR_second_subtask_dataset``is for the pragmatic 
identification and reasoning (PIR) task, and ``CQA_task_dataset`` is for the conversational question answering (CQA)
task. As for ``NLI_task_dataset`` and ``NLI_dataset_without_context``, they are data for zero-NLI, while the latter
is a context-removed version.




## Dataset Creation


### Source Data
We leverage the data of [INTERVIEW dataset](https://www.kaggle.com/datasets/shuyangli94/interview-npr-media-dialog-transcripts) collected by 
Majumder et al as our source.
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->

### Annotating Process

<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
#### Step I. Automatic Selection:
The extensive size of the source dataset introduces redundancy, 
and thus requires automatic measures to alleviate the burden of human annotation. 
Therefore, we employ algorithms and models to perform an initial filtering process.
#### Step II. Fine-grained Annotation:
We leverage Amazon Mechanical Turk to conduct detailed annotations of pragmatic turns within our dialogues. 
Workers participating in the annotation task are instructed to select 
all turns that exhibit a divergence between their literal meaning and their intended meaning. 
Due to the subjective nature of pragmatic reasoning, we request the workers to provide confidence scores 
along with  reasons for their choices.
#### Step III. Human Refinement:
In this process, tasks for workers are formulated as multiple-choice questions. 
Previously collected human-annotated reasons are transformed into choices, utilizing a template format: 
[turn {turn_id}: {reason}]. In addition, to mitigate the impact of careless workers, 
we introduce a distractor choice for each gold choice.




## Citation 

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
```
@inproceedings{li2023diplomat,
    title={DiPlomat: A Dialogue Dataset for Situated Pragmatic Reasoning},
    author={Hengli Li, Song-Chun Zhu, Zilong Zheng},
    booktitle={Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
    year={2023}
}
```

## Dataset Card Contact

If there is any problem with the dataset, please email [lihengli@stu.pku.edu.cn](mailto: 2000017754@stu.pku.edu.cn).