
# Dataset Card for GPT-Negochat

## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Fields](#data-fields)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)

## Dataset Description

- **Repository:** https://github.com/msamogh/GPT-NegoChat-Corpus
- **Point of Contact:** msamogh@gmail.com

### Dataset Summary
he **GPT-Negochat** corpus is a modified version of the original Negochat corpus (https://aclanthology.org/L16-1501/), which contains negotiation dialogues between an Employer and a Candidate. The utterances in the original corpus were generated using a template-based NLG module and therefore, sound robotic and in general, do not sound convincingly real.

GPT-Negochat is the result of using GPT-3 to modify this original corpus to make the dialogues resemble actual job-negotiation dialogues more closely while still retaining the original meaning of the utterances.

In addition to rephrasing the utterances, a small set of highly unrealistic dialogue segments have been removed in GPT-Negochat without affecting the coherence of the surrounding dialogue.

### Supported Tasks and Leaderboards

- Dialogue Act Classification
- Offer Identification
- Agreement Tracking

### Languages

- English

## Dataset Structure

### Data Fields

Below is an excerpt containing two consecutive turns from a dialogue. The `input` field contains the utterance from the original Negochat corpus. The `augmented_input` field contains the same utterance rephrased using GPT-3.

```json
{
    "role": "Candidate",
    "input": "I want a position of project manager",
    "output": [
        {
            "Offer": {
                "Job Description": "Project Manager"
            }
        }
    ],
    "augmented_input": "I'm interested in a project manager role."
},
{
    "role": "Employer",
    "input": "I do have programmer positions open with a strong potential to advance to project manager based on your performance.",
    "output": [
        {
            "Offer": {
                "Job Description": "Programmer"
            }
        }
    ],
    "augmented_input": "We do have programmer roles available that could provide you with the opportunity to advance to project manager based on your performance. "
}
```

## Dataset Creation

### Curation Rationale

The original Negochat corpus is one of the only dialogue corpora with containing turn-level annotations for offers, acceptances, and rejects in a negotiation dialogue.
However, the utterances in the corpus were generated using a template-based NLG system, which makes the dialogues unrealistic to the point of sounding robotic at times.
We wanted to make the utterances sound more like those from an actual negotiation dialogue in a job interview.

### Source Data

#### Initial Data Collection and Normalization

The original Negochat corpus can be found here: [https://github.com/vaskonov/negochat_corpus](https://github.com/vaskonov/negochat_corpus)

## Annotations
Since each utterance in GPT-Negochat was generated by rephrasing the original without changing the underlying meaning, we simply transfer over the annotations from the original Negochat corpus.
license:apache-2.0 region:us
{"license": "apache-2.0"}
TAGS
#license-apache-2.0 #region-us 


# Dataset Card for GPT-Negochat

## Table of Contents
- Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Fields
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations

## Dataset Description

- Repository: URL
- Point of Contact: msamogh@URL

### Dataset Summary
he GPT-Negochat corpus is a modified version of the original Negochat corpus (URL which contains negotiation dialogues between an Employer and a Candidate. The utterances in the original corpus were generated using a template-based NLG module and therefore, sound robotic and in general, do not sound convincingly real.

GPT-Negochat is the result of using GPT-3 to modify this original corpus to make the dialogues resemble actual job-negotiation dialogues more closely while still retaining the original meaning of the utterances.

In addition to rephrasing the utterances, a small set of highly unrealistic dialogue segments have been removed in GPT-Negochat without affecting the coherence of the surrounding dialogue.

### Supported Tasks and Leaderboards

- Dialogue Act Classification
- Offer Identification
- Agreement Tracking

### Languages

- English

## Dataset Structure

### Data Fields

Below is an excerpt containing two consecutive turns from a dialogue. The 'input' field contains the utterance from the original Negochat corpus. The 'augmented_input' field contains the same utterance rephrased using GPT-3.



## Dataset Creation

### Curation Rationale

The original Negochat corpus is one of the only dialogue corpora with containing turn-level annotations for offers, acceptances, and rejects in a negotiation dialogue.
However, the utterances in the corpus were generated using a template-based NLG system, which makes the dialogues unrealistic to the point of sounding robotic at times.
We wanted to make the utterances sound more like those from an actual negotiation dialogue in a job interview.

### Source Data

#### Initial Data Collection and Normalization

The original Negochat corpus can be found here: URL

## Annotations
Since each utterance in GPT-Negochat was generated by rephrasing the original without changing the underlying meaning, we simply transfer over the annotations from the original Negochat corpus.
# Dataset Card for GPT-Negochat ## Table of Contents
- Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Fields
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations ## Dataset Description

- Repository: URL
- Point of Contact: msamogh@URL ### Dataset Summary
he GPT-Negochat corpus is a modified version of the original Negochat corpus (URL which contains negotiation dialogues between an Employer and a Candidate. The utterances in the original corpus were generated using a template-based NLG module and therefore, sound robotic and in general, do not sound convincingly real.

GPT-Negochat is the result of using GPT-3 to modify this original corpus to make the dialogues resemble actual job-negotiation dialogues more closely while still retaining the original meaning of the utterances.

In addition to rephrasing the utterances, a small set of highly unrealistic dialogue segments have been removed in GPT-Negochat without affecting the coherence of the surrounding dialogue. ### Supported Tasks and Leaderboards

- Dialogue Act Classification
- Offer Identification
- Agreement Tracking ### Languages

- English ## Dataset Structure ### Data Fields

Below is an excerpt containing two consecutive turns from a dialogue. The 'input' field contains the utterance from the original Negochat corpus. The 'augmented_input' field contains the same utterance rephrased using GPT-3. ## Dataset Creation ### Curation Rationale

The original Negochat corpus is one of the only dialogue corpora with containing turn-level annotations for offers, acceptances, and rejects in a negotiation dialogue.
However, the utterances in the corpus were generated using a template-based NLG system, which makes the dialogues unrealistic to the point of sounding robotic at times.
We wanted to make the utterances sound more like those from an actual negotiation dialogue in a job interview. ### Source Data #### Initial Data Collection and Normalization

The original Negochat corpus can be found here: URL ## Annotations
Since each utterance in GPT-Negochat was generated by rephrasing the original without changing the underlying meaning, we simply transfer over the annotations from the original Negochat corpus.
TAGS
#license-apache-2.0 #region-us 
 # Dataset Card for GPT-Negochat ## Table of Contents
- Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Fields
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations ## Dataset Description

- Repository: URL
- Point of Contact: msamogh@URL ### Dataset Summary
he GPT-Negochat corpus is a modified version of the original Negochat corpus (URL which contains negotiation dialogues between an Employer and a Candidate. The utterances in the original corpus were generated using a template-based NLG module and therefore, sound robotic and in general, do not sound convincingly real.

GPT-Negochat is the result of using GPT-3 to modify this original corpus to make the dialogues resemble actual job-negotiation dialogues more closely while still retaining the original meaning of the utterances.

In addition to rephrasing the utterances, a small set of highly unrealistic dialogue segments have been removed in GPT-Negochat without affecting the coherence of the surrounding dialogue. ### Supported Tasks and Leaderboards

- Dialogue Act Classification
- Offer Identification
- Agreement Tracking ### Languages

- English ## Dataset Structure ### Data Fields

Below is an excerpt containing two consecutive turns from a dialogue. The 'input' field contains the utterance from the original Negochat corpus. The 'augmented_input' field contains the same utterance rephrased using GPT-3. ## Dataset Creation ### Curation Rationale

The original Negochat corpus is one of the only dialogue corpora with containing turn-level annotations for offers, acceptances, and rejects in a negotiation dialogue.
However, the utterances in the corpus were generated using a template-based NLG system, which makes the dialogues unrealistic to the point of sounding robotic at times.
We wanted to make the utterances sound more like those from an actual negotiation dialogue in a job interview. ### Source Data #### Initial Data Collection and Normalization

The original Negochat corpus can be found here: URL ## Annotations
Since each utterance in GPT-Negochat was generated by rephrasing the original without changing the underlying meaning, we simply transfer over the annotations from the original Negochat corpus.
passage: TAGS
#license-apache-2.0 #region-us 
# Dataset Card for GPT-Negochat## Table of Contents
- Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Fields
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations## Dataset Description

- Repository: URL
- Point of Contact: msamogh@URL### Dataset Summary
he GPT-Negochat corpus is a modified version of the original Negochat corpus (URL which contains negotiation dialogues between an Employer and a Candidate. The utterances in the original corpus were generated using a template-based NLG module and therefore, sound robotic and in general, do not sound convincingly real.

GPT-Negochat is the result of using GPT-3 to modify this original corpus to make the dialogues resemble actual job-negotiation dialogues more closely while still retaining the original meaning of the utterances.

In addition to rephrasing the utterances, a small set of highly unrealistic dialogue segments have been removed in GPT-Negochat without affecting the coherence of the surrounding dialogue.### Supported Tasks and Leaderboards

- Dialogue Act Classification
- Offer Identification
- Agreement Tracking### Languages

- English## Dataset Structure### Data Fields

Below is an excerpt containing two consecutive turns from a dialogue. The 'input' field contains the utterance from the original Negochat corpus. The 'augmented_input' field contains the same utterance rephrased using GPT-3.## Dataset Creation### Curation Rationale

The original Negochat corpus is one of the only dialogue corpora with containing turn-level annotations for offers, acceptances, and rejects in a negotiation dialogue.
However, the utterances in the corpus were generated using a template-based NLG system, which makes the dialogues unrealistic to the point of sounding robotic at times.
We wanted to make the utterances sound more like those from an actual negotiation dialogue in a job interview.### Source Data