SafeConv: Explaining and Correcting Conversational Unsafe Behavior

Resources for our ACL 2023 SafeConv paper.

What is SafeConv?

SafeConv is a large-scale dataset (160000 prompt-response pairs) with comprehensive annotations for conversational safety:

1. binary safety label of the prompt;
2. binary safety label of the response;
3. unsafe spans in the response;
4. safe alternatives for the unsafe responses.

Data

The splited data is in data/. Each line is an instance, which is a dictionary. Below are the meaning of the keys:

Key	Meaning
source	data source; 'P' denotes Pchatbot; 'L' denotes LCCC
prompt	dialogue history
response	current utterance
prompt_label	binary safety label of the prompt
response_label	binary safety label of the response
unsafe_spans_indices	[start, end] indices of the unsafe spans in the response
rewrites	rewritten utterances

Cite

You could cite our paper if you find the dataset is helpful using this BibTeX:

@inproceedings{zhang-etal-2023-safeconv,
    title = "{S}afe{C}onv: Explaining and Correcting Conversational Unsafe Behavior",
    author = "Zhang, Mian  and
      Jin, Lifeng  and
      Song, Linfeng  and
      Mi, Haitao  and
      Chen, Wenliang  and
      Yu, Dong",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.2",
    pages = "22--35",
}