Data of the paper "BETOLD: A Task-Oriented Dialog Dataset for Dialog Breakdown"

BETOLD (Breakdown Expectation for Task-Oriented Long Dialogs) is a task-oriented dialog dataset, derived from real conversations between system and user in order to fulfill the task of booking an appointment. The aim of the dataset is to predict LUFHs, i.e. user-initiated (U) forward calls (F) and hang-ups (H) that happen in a late (L) point of the conversation. This dataset is characterized by NLG and NLU intents and entities. It does not provide textual utterances.

This work has been accepted at the COLING 2022's workshop "When creative AI meets conversational AI". Click here to read the paper!

Dataset

Dataset Features

All dialogs include:

• LUHF: binary label 'luhf' or 'not_luhf'.
• utterances_annotations: contains all NLU and NLG intents and entities.
  • caller_name: indicates if it is a NLU (i.e a user input) or NLG (i.e a system input).
  • intent: the purpose which the speaker wants to achieve.
  • entities: slots extracted from NLU and NLG giving context to the conversation.

Structure

The dataset is structured in the following way:

[
  {
    "LUHF": "luhf", # binary label 'luhf' or 'not_luhf'
    "utterances_annotations": [ # list of interactions between system and human
      {
        "caller_name": "nlg",
        "intent": "intro_assistant_1",
        "entities": [
          {
            "entity": "recording_warning"
          }
        ]
      },
      {
        "caller_name": "nlg",
        "intent": "intro_assistant_2",
        "entities": [
        ]
      },
      {
        "caller_name": "nlu",
        "intent": "schedule",
        "entities": [
        ]
      },
      # ...
    ]
  },
  # ...
]

See the file BETOLD_description.md and the paper for more details on the creation of the dataset and on the features.

Models

See this repository for the models' implementation: https://github.com/telepathylabsai/dialog_breakdown_detection

Team

• Silvia Terragni <silvia.terragni@telepathy.ai>
• Bruna Guedes
• Andre Manso
• Modestas Filipavicius
• Nghia Khau
• Roland Mathis

How to cite this work

This work has been accepted at the COLING 2022's workshop When creative AI meets conversational AI. If you decide to use this resource, please cite:

@inproceedings{terragni2022_betold,
    title = "{BETOLD}: A Task-Oriented Dialog Dataset for Breakdown Detection",
    author = "Terragni, Silvia  and
      Guedes, Bruna  and
      Manso, Andre  and
      Filipavicius, Modestas  and
      Khau, Nghia  and
      Mathis, Roland",
    booktitle = "Proceedings of the Second Workshop on When Creative AI Meets Conversational AI",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.cai-1.4",
    pages = "23--34",
}