Life Event Dialog (LED)

Dataset for LED: A Dataset for Life Event Extraction from Dialogs.

In this paper, we present Life Event Dialog, a dataset containing fine-grained life event annotations on conversational data from DailyDialog dataset.

Example

Structure

Each line is a jsonl object of a conversation with the following format:

• DailyDialog_id: The dialogue id to map to the original dialogue in DialyDialog dataset.
• dialog_id: A unique identifier for each dialogue in LifeEventDialog_[train/valid/test].jsonl
• events: A list of Events in each turn.
• coreferences: A list of clusters, each cluster is a list of tokens_id. Note that the 1st cluster must be the cluster of speaker 1 (S1) and the 2nd must be of speaker 2 (S2). The cluster of S1/S2 might be an empty cluster if no events happen to S1/S2.

Each Event consists of the following schema:

"event_type": {
    'explicit': Bool, # 1: explicit, 0: implicit
    'Verb': {
        'tokens': Str,
        'tokens_id': Str, # Format: f'{start_index}-{end_index}'
    },
    'Class': Str,
    'Frame': Str,
}
"participant": {
    'subject': EntityDict,
    'object': EntityDict,
},
"event_status": { 
    'polarity': Bool, # 1: positive, 0: negative
    'modality': Bool, # 1: actual, 0: hypothetical
    'time': Str or Dict, # ['BEFORE', 'NOW', 'AFTER', 'CONTINUOUSLY', or an EntityDict refers to time in the dialogue

where

EntityDict = {
    "entity_id": Int, # which is also the index of cluster in the "coreferences" field
    "tokens": Str,
    "tokens_id": Str, # Foramt: f'{start_index}' or f'{start_index}-{end_index}', e.g. '0' or '0-1'
}

License

This dataset is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0.).

This means you are free to use and share the dataset for non-commercial purposes, as long as you give appropriate credit to the original creators and share any adaptations of the dataset under the same license.

Citation

If you use this dataset in your research, please cite our paper:

@inproceedings{chen-etal-2023-led,
    title = "{LED}: A Dataset for Life Event Extraction from Dialogs",
    author = "Chen, Yi-Pei  and
      Yen, An-Zi  and
      Huang, Hen-Hsen  and
      Nakayama, Hideki  and
      Chen, Hsin-Hsi",
    booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-eacl.29",
    pages = "384--398",
}