Hugging Face's logo

Datasets:
Orange
/
rdfdial

Tasks:
Text Generation
Text2Text Generation
Modalities:
Text
Formats:
json
Sub-tasks:
conversational
rdf-to-text
dialogue-generation
Languages:
English
Size:
10K - 100K
Tags:
dialogue
rdf
dst
Libraries:
Datasets
pandas
License:
Dataset card Viewer Files Community
1
rdfdial / README.md
mveyret's picture
mveyret
fix: invalid path
de3734e
preview code
|
raw
history blame contribute delete
9.36 kB
metadata
configs:
  - config_name: bundle-converted
    description: Merge of all rdf converted datasets
    data_files:
      - path:
          - dstc2-rdf/train.jsonl
          - multiwoz-rdf/train.jsonl
          - sfxdial-rdf/train.jsonl
        split: train
      - path:
          - dstc2-rdf/test.jsonl
          - multiwoz-rdf/test.jsonl
          - sfxdial-rdf/test.jsonl
        split: test
      - path:
          - dstc2-rdf/validation.jsonl
          - multiwoz-rdf/validation.jsonl
          - sfxdial-rdf/validation.jsonl
        split: validation
  - config_name: bundle-simulated
    description: Merge of all rdf simulated datasets
    data_files:
      - path:
          - camrest-sim-rdf/train.jsonl
          - multiwoz-sim-rdf/train.jsonl
        split: train
      - path:
          - camrest-sim-rdf/test.jsonl
          - camrest-sim-rdf/test.jsonl
        split: test
      - path:
          - camrest-sim-rdf/validation.jsonl
          - multiwoz-sim-rdf/validation.jsonl
        split: validation
  - config_name: dstc2
    description: DSTC2 converted to rdf format
    data_files:
      - path: dstc2-rdf/train.jsonl
        split: train
      - path: dstc2-rdf/test.jsonl
        split: test
      - path: dstc2-rdf/validation.jsonl
        split: validation
  - config_name: sfxdial
    description: Sfxdial converted to rdf format
    data_files:
      - path: sfxdial-rdf/train.jsonl
        split: train
      - path: sfxdial-rdf/test.jsonl
        split: test
      - path: sfxdial-rdf/validation.jsonl
        split: validation
  - config_name: multiwoz
    description: MultiWoz converted to rdf format
    data_files:
      - path: multiwoz-rdf/train.jsonl
        split: train
      - path: multiwoz-rdf/test.jsonl
        split: test
      - path: multiwoz-rdf/validation.jsonl
        split: validation
  - config_name: camrest-sim
    description: Synthetic dialogs on the Cambridge restaurant search domain
    data_files:
      - path: camrest-sim-rdf/train.jsonl
        split: train
      - path: camrest-sim-rdf/test.jsonl
        split: test
      - path: camrest-sim-rdf/validation.jsonl
        split: validation
  - config_name: multiwoz-sim
    description: Synthetic dialogs on the Multiwoz domains
    data_files:
      - path: multiwoz-sim-rdf/train.jsonl
        split: train
      - path: multiwoz-sim-rdf/test.jsonl
        split: test
      - path: multiwoz-sim-rdf/validation.jsonl
        split: validation
tags:
  - dialogue
  - rdf
  - dst
task_categories:
  - text-generation
  - text2text-generation
task_ids:
  - conversational
  - rdf-to-text
  - dialogue-generation
license:
  - other
packages:
  - python-gitlab
language:
  - en

Dataset Card for rdfdial

Table of Contents

Dataset Description

Dataset Summary

This dataset provides dialogues annotated in dialogue acts and dialogue state in and RDF based formalism.

There is a conversion of sfxdial, dstc2 and multiwoz2.3 datasets as well as two fully synthetic datasets created from simulated conversations: camrest-sim and multiwoz-sim.

Original dataset before conversion are available here:

Supported Tasks and Leaderboards

This dataset was used for the following tasks:

  • Natural Language Generation
  • Dialogue State Tracking

Languages

This dataset includes the following languages:

  • English

Dataset Structure

Data Instances

For all datasets, each item has this schema:

{
    "dialogue_id": "string",         # dialog identifier
    "turns": [{                      # list of dialog turns
        "id": "int8",                # dialog turn index in the conversation
        "speaker": "string",         # speaker identifier ('user' or 'system')
        "text": "string",            # speaker utterance
        "rdf-acts": ["string"],      # string representation of dialog acts
    }],
    "states": [{                     # dialog states for each turn
        "id": "int8",
        "multi_relations": "bool",   # are multiple instances of relations allowed ?
        "triples": [["string"]],     # triples representing the state
        "turn_ids": ["int8"],        # ids of turns contributing to this state
    }],
}

Data Fields

For each dataset item, the following fields are provided:

  • dialogue_id: unique dialogue identifier
  • turns: list of speech turns, each turn contains the following fields:
    • id: turn index in the dialogue
    • speaker: identifier for the speaker (user or system)
    • text: turn utterance
    • rdf-acts: list of dialogue acts using string representation of rdf formalism each act has the form: act(triple;...) where triple is formatted as (subject,predicate,object)
  • states: list of states for the dialogue, each entry contains the following fields:
    • id: state index in the dialogue
    • multi_relations: boolean indicating if multiple instances of the same predicate are allowed or not
    • triples: list of triples representing the graph state, each triple is a list of 3 string like [subject,predicate,object]
    • turn_ids: list of turn ids that contributed to this state

Data Splits

For each dataset, splits were generated randomly in the following proportions:

  • train: 80%
  • validation: 16%
  • test: 4%

Dataset Creation

Curation Rationale

This dataset has been created to work with graph base dialog state representation using generative models (T5 family).

Source Data

Initial Data Collection and Normalization

Who are the source language producers?

  • Converted datasets: see original datasets documentation
  • Synthetic datasets: conversations were generated using an agenda-based user simulator and a rule based agent working directly with dialogue acts. These conversations were then augmented with natural language user/system utterances. Natural language generation was done using a T5-base model fine-tuned on the converted datasets.

Annotations

Annotation process

  • Converted datasets: rule-based conversion of the user/system dialogue acts from slot-value to RDF based format. The dialogue state is created automatically using another rule based tracked working with triples. Some conversations could not be converted automatically and/or contained wrong/confusing annotations and were removed from the dataset compared to the original ones.
  • Synthetic datasets: simulation work at the annotation level and the dataset was augmented to include natural language information.

Who are the annotators?

All annotations were generated automatically.

For dialogue acts:

  • converted data: rules were applied to convert slot-value based dialogue acts to rdf-based ones
  • synthetic data: rdf-based dialogue acts were directly generated by the dialogue simulation.

For dialogue states, a rule based system was using taking rdf-based dialogue acts as its inputs.

Personal and Sensitive Information

This dataset does not contains any personal or sensitive information.

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

Converted datasets follow their original licenses:

Simulated conversation are provided with the following licenses:

Citation Information

[More Information Needed]

Contributions

  • Morgan Veyret