Hugging Face's logo

Datasets:
henry12348
/
DiPlomat

Languages:
English
ArXiv:
License:
Dataset card Files Community
DiPlomat / README.md
henry12348's picture
henry12348
Update README.md
8fc673f
preview code
|
raw
history blame contribute delete
No virus
6.51 kB
metadata
language:
  - en
license: cc-by-nc-sa-4.0
pretty_name: DiPlomat
dataset_info:
  - config_name: CQA
    features:
      - name: text
        sequence: string
      - name: speaker
        sequence: string
      - name: gold_statement
        dtype: string
      - name: questions
        dtype: string
      - name: answer
        dtype: string
    splits:
      - name: train
        num_bytes: 19805284
        num_examples: 15585
      - name: validation
        num_bytes: 1959148
        num_examples: 1559
      - name: test
        num_bytes: 2967746
        num_examples: 2338
    download_size: 25566918
    dataset_size: 24732178
  - config_name: NLI_with_context
    features:
      - name: dialogue
        sequence: string
      - name: speaker
        sequence: string
      - name: human answer
        dtype: string
    splits:
      - name: train
        num_bytes: 2977929
        num_examples: 2551
    download_size: 3042193
    dataset_size: 2977929
  - config_name: NLI_without_context
    features:
      - name: text
        dtype: string
      - name: hypothesis
        dtype: string
    splits:
      - name: train
        num_bytes: 1095335
        num_examples: 2551
    download_size: 1146864
    dataset_size: 1095335
  - 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: 4366468
    dataset_size: 4291965
  - config_name: PIR_second
    features:
      - name: text
        sequence: string
      - name: speaker
        sequence: string
      - name: correct_turn_number
        dtype: int64
      - name: label
        dtype: int64
      - name: choice
        sequence: string
    splits:
      - name: train
        num_bytes: 9263111
        num_examples: 5188
      - name: validation
        num_bytes: 399924
        num_examples: 244
      - name: test
        num_bytes: 1890798
        num_examples: 1062
    download_size: 11740508
    dataset_size: 11553833

DiPlomat

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 (CQA). Furthermore, we provide the data that we use for zero-NLI.

Dataset Sources

Dataset Structure

Field Task
PIR_first Pragmatic Identification and Reasoning Subtask1
PIR_second Pragmatic Identification and Reasoning Subtask2
CQA Conversational Question Answering
NLI_with_context Zero-Shot NLI with context
NLI_without_context Zero-Shot NLI without context

NOTE: If you'd like to test on the whole PIR task, please don't change the order of PIR Subtask 1's and Subtask 2's test file's data, as both of them are deliberately arranged as the same order.

Dataset Creation

Source Data

We leverage the data of INTERVIEW dataset collected by Majumder et al as our source.

Annotating Process

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 

@inproceedings{li2023diplomat,
    title={DiPlomat: A Dialogue Dataset for Situated Pragmatic Reasoning},
    author={Hengli Li and Song-Chun Zhu and 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).