metadata

annotations_creators:
  - found
language_creators:
  - found
language:
  - en
license:
  - gpl-3.0
multilinguality:
  - monolingual
size_categories:
  - 100K<n<1M
source_datasets:
  - original
task_categories:
  - text-generation
  - fill-mask
  - text-retrieval
task_ids:
  - dialogue-modeling
  - utterance-retrieval
paperswithcode_id: pec
pretty_name: Persona-Based Empathetic Conversational
dataset_info:
  - config_name: happy
    features:
      - name: personas
        sequence: string
      - name: context
        sequence: string
      - name: context_speakers
        sequence: string
      - name: response
        dtype: string
      - name: response_speaker
        dtype: string
    splits:
      - name: train
        num_bytes: 643196978
        num_examples: 157195
      - name: test
        num_bytes: 92003042
        num_examples: 22730
      - name: validation
        num_bytes: 81132088
        num_examples: 19829
    download_size: 252434681
    dataset_size: 816332108
  - config_name: offmychest
    features:
      - name: personas
        sequence: string
      - name: context
        sequence: string
      - name: context_speakers
        sequence: string
      - name: response
        dtype: string
      - name: response_speaker
        dtype: string
    splits:
      - name: train
        num_bytes: 518616402
        num_examples: 123968
      - name: test
        num_bytes: 64173390
        num_examples: 15324
      - name: validation
        num_bytes: 66675909
        num_examples: 16004
    download_size: 252434681
    dataset_size: 649465701
  - config_name: all
    features:
      - name: personas
        sequence: string
      - name: context
        sequence: string
      - name: context_speakers
        sequence: string
      - name: response
        dtype: string
      - name: response_speaker
        dtype: string
    splits:
      - name: train
        num_bytes: 1162655628
        num_examples: 281163
      - name: test
        num_bytes: 156310498
        num_examples: 38054
      - name: validation
        num_bytes: 147940164
        num_examples: 35833
    download_size: 252434681
    dataset_size: 1466906290
config_names:
  - all
  - happy
  - offmychest

Dataset Card for PEC

Dataset Description
Dataset Structure
Dataset Creation
Considerations for Using the Data
Additional Information

Dataset Description

Repository: PEC repository
Paper: Towards Persona-Based Empathetic Conversational Models
Point of Contact: Peixiang Zhong

Dataset Summary

The PEC dataset is an English-language dataset of open-domain conversations gathered from two subreddits on Reddit, i.e., happy and offmychest. PEC has around 350K persona-based empathetic conversations. Each utterance is associated with a speaker, and each speaker has a persona of multiple persona sentences. The conversations in PEC are more empathetic than casual conversations. The conversations in the happy domain are mostly positive, whereas the conversations in the offmychest domain are mostly negative.

Supported Tasks and Leaderboards

dialogue-modeling, utterance-retrieval: this dataset can be used to train a generative or retrieval-based conversational model.

Languages

English

Dataset Structure

Data Instances

A typical data example comprises a list of context utterances, a list of context speakers, a response to the context, the response speaker and the persona of the response speaker.

An example from PEC looks as follows:

{'context': ['found out this morning i got a job promotion ! ! !'],
 'context_speakers': ['HeWentToJared91'],
 'personas': [
  "i ca n't stand working in the ugli .",
  'i ’ve always liked my eyes except for the fact that they ca n’t shoot lasers',
  'i feel really bad about myself as a person right now , and i could really use a hand .',
  'i drank a coffee , and it just made me feel even more exhausted .',
  'i want a natsuki t shirt',
  "i 've dealt with depression in the past .",
  'i love red dead 2'],
 'response': "you look like a nice person ! we 're proud of you , and i bet you earned that promotion !",
 'response_speaker': 'tylock'}

Data Fields

context: a list of strings, each string denotes a context utterance.
context_speakers: a list of strings, each string denotes a speaker.
response: a string denoting the response to the context.
response_speaker: a string denoting the speaker of response.
personas: a list of strings, each string denotes a persona sentence of response_speaker.

Data Splits

The data is split into a training, validation and test set for each of the three domains. Note that the all domain is the concatenation of the happy and offmychest domains.

domain	train	validation	test
happy	157195	19829	22730
offmychest	123968	16004	15324
all	281163	35833	38054

Dataset Creation

Curation Rationale

PEC was built to provide a testbed for machines to learn persona-based empathetic responding. In our empirical analysis, we found that different personas have different styles of empathetic responding. This dataset can also be used to investigate the link between persona and empathy in human conversations. According to our human assessment, the conversations on the happy and offmychest subreddits are significantly more empathetic than casual conversations.

Source Data

Initial Data Collection and Normalization

The data was obtained via the pushshift API via Google BigQuery.

Who are the source language producers?

The language producers are users of the r/happy, and r/offmychest subreddits between 2012 and 2020. No further demographic information was available from the data source.

Annotations

Annotation process

The dataset does not contain any additional annotations.

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

The dataset includes the speaker IDs of users on happy and offmychest subreddits.

Considerations for Using the Data

Social Impact of Dataset

The purpose of this dataset is to help develop more personalised and empathetic conversational systems, which is an important milestone towards truly human-like conversational agents.

Discussion of Biases