to-be/annomi-motivational-interviewing-therapy-conversations

Dataset Card for Dataset Name

Converted the AnnoMI motivational interviewing dataset into sharegpt format. It is the first public collection of expert-annotated MI transcripts. Source.

Dataset Details

Dataset Description

AnnoMI, containing 133 faithfully transcribed and expert-annotated demonstrations of high- and low-quality motivational interviewing (MI), an effective therapy strategy that evokes client motivation for positive change.

Sample conversation number 40, regarding drug use:

client
Pretty good. It's been three months now.

therapist
Nice.

client
Um, and—

therapist
Since the-- since you used, uh-- since the last time you used meth, right?

client
Right.

therapist
Okay.

client
Right. It's been three months, and I've been doing pretty well.

therapist
Good.

client
Um, and I met a couple new friends.

therapist
Nice.

client
So—

therapist
So-so, uh, have-- last time we talked, you know, you were kind of cutting the-the ex-boyfriend out. This is because he was your dealer, so--

client
Right.

therapist
And how is that going for you?

client
Right. It's been good. I haven't answered the phone when he's called-

therapist
Okay.

client
-and a couple of my other friends have tried to call me as well, and I haven't answered the phone. Um, so it's been good for me to just reach out and like, meet a couple of new people, um, in the neigh-neighborhood that I'm living in.

therapist
So it sounds like you've made some good changes. The change in your environment, and some of your behaviors, and, uh, and these new friends.

client
Yeah, the new friends, and I've been using the, you know, the breathing exercises you taught me, when I get-- start to feel a little stressed out, and I've been- I've been using those and it helps me. I go for a walk and do my breathing exercises, and it calms me down and kind of gets me through that time.

therapist
So it sounds like--

Dataset Sources

• Repository: [https://github.com/uccollab/AnnoMI]
• Paper [optional]: [https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9746035]

Uses

Training on longer conversations and medical advice.

Direct Use

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Dataset Structure

Sharegpt format to allow for long conversations back and forth.

therapist is gpt

client is human

id is a concatenation of:

• transcript_id: the unique numerical identifier of the conversation/transcript where this utterance belongs. Note that this identifier is NOT used for ordering, and it is only to distinguish between different conversations in the dataset.
• video_url: the URL of the original video of the conversation/transcript where this utterance belongs.
• topic: the topic(s) of the conversation/transcript where this utterance belongs.

[
  {
    "id": "40_https://www.youtube.com/watch?v=wcu8oqHIsbc_reducing drug use",
    "conversations": [
      {
        "from": "human",
        "value": "Pretty good. It's been three months now"
      },
      {
        "from": "gpt",
        "value": "Nice."
      },
      {
        "from": "human",
        "value": "Right. It's been three months, and I've been doing pretty well."
      },
      {
        "from": "gpt",
        "value": "So-so, uh, have-- last time we talked, you know, you were kind of cutting the-the ex-boyfriend out. This is because he was your dealer, so--"
      },
      ...
]

Dataset Creation

Curation Rationale

Despite the remarkable growth of research in recent years on the analysis of counselling conversations through natural language processing methods, the potential of this field has been greatly limited by the lack of access to publicly available therapy dialogues, especially those with expert annotations.

Personal and Sensitive Information

It is anonymized.

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

Citation [optional]

@INPROCEEDINGS{9746035, author={Wu, Zixiu and Balloccu, Simone and Kumar, Vivek and Helaoui, Rim and Reiter, Ehud and Reforgiato Recupero, Diego and Riboni, Daniele}, booktitle={ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, title={Anno-MI: A Dataset of Expert-Annotated Counselling Dialogues}, year={2022}, volume={}, number={}, pages={6177-6181}, doi={10.1109/ICASSP43922.2022.9746035}} }

Dataset Card Contact

Toon Beerten (toon@neontreebot.be)