
# SPOLIN
[![CC BY-NC 4.0][cc-by-nc-shield]][cc-by-nc]


## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Available SPOLIN Versions](#available_spolin_versions)
  - [Relevant Links](#relevant-links)
- [Dataset Structure](#dataset-structure)
- [Dataset Statistics](#dataset-statistics)
- [Other Information](#other-information)
  - [ACL Presentation](#acl-presentation)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)

## Dataset Description 

### Dataset Summary 
This is the repo for the paper ["Grounding Conversations with Improvised Dialogues"](https://aclanthology.org/2020.acl-main.218/) (ACL2020). 
The _Selected Pairs of Learnable ImprovisatioN_ (SPOLIN) corpus is a collection of more than 68,000 "Yes, and" type dialogue pairs extracted from the Spontaneanation podcast by Paul F. Tompkins, the Cornell Movie-Dialogs Corpus, and the SubTle corpus. For more information, refer to our [paper](https://arxiv.org/abs/2004.09544) or our [project page](https://justin-cho.com/spolin).


### Available SPOLIN Versions:
The core dataset that was used for the experiments in the paper only includes _yes-ands_ and non-_yes-ands_ from Spontaneanation and most of what is provided in those extracted from the Cornell Movie-Dialogs Corpus. After the submitting the paper, we continued our iterative data augmentation process, repeating another iteration with the Cornell Movie-Dialogs Corpus and extracting from the SubTle corpus. This expanded version is also included in this repository [here](/data). This latest version of SPOLIN was used to train the model used in our [demo](https://spolin.isi.edu). 


In the `data` folder, we provide two versions of the SPOLIN training set: 
1. Version used for experiments in the ACL paper: `data/spolin-train-acl.csv` 
2. Expanded version: `data/spolin-train.csv`

### Relevant Links: 

* Project page: https://justin-cho.com/spolin
* Github repo: https://github.com/wise-east/spolin 
* SpolinBot Demo: https://spolin.isi.edu
* ACL2020 Paper: https://aclanthology.org/2020.acl-main.218/


## Dataset Structure
**Fields**
* `id`: unique identifier
* `prompt`: first utterance in utterance pair
* `response`: second utterance in utterance pair 
* `label`: yesand = 1, non-yesand = 0 
* `source`: the source for the sample 
* `split`: whether the sample belongs to the training set or the validation set 
  
## Dataset Statistics 

##### `spolin-train.csv`:  
|| yesands| non-yesands|
|--|---:|---:|
|Spontaneanation|10,459|5,587*|
|Cornell|16,426|18,310|
|SubTle|40,303|19,512|
|Total|67,188|43,409|


##### `spolin-train-acl.csv`: 

|| yesands| non-yesands|
|--|---:|---:|
|Spontaneanation|10,459|5,587*|
|Cornell|14,976|17,851|
|Total|25,435|23,438|

##### `spolin-valid.csv`: 

|| yesands| non-yesands|
|--|---:|---:|
|Spontaneanation|500|500*|
|Cornell|500|500|
|Total|1,000|1,000|

\*Artificially collected by mix & matching positive Spontaneanation samples to balance dataset for training classifier

## Other Information 

### ACL Presentation 

[Video recording](https://slideslive.com/38928948/grounding-conversations-with-improvised-dialogues)


### Citation Information

If you use our data for your work, please cite our ACL2020 paper: 
```
@inproceedings{cho2020spolin,
    title={Grounding Conversations with Improvised Dialogues},
    author={Cho, Hyundong and May, Jonathan},
    booktitle ={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
    publisher = {Association for Computational Linguistics}, 
    location =  {Seattle, Washington, USA},
    year={2020}
}  
```


### Licensing Information


This work is licensed under a [Creative Commons Attribution-NonCommercial 4.0 International License][cc-by-nc].

[![CC BY-NC 4.0][cc-by-nc-image]][cc-by-nc]

[cc-by-nc]: http://creativecommons.org/licenses/by-nc/4.0/
[cc-by-nc-image]: https://licensebuttons.net/l/by-nc/4.0/88x31.png
[cc-by-nc-shield]: https://img.shields.io/badge/License-CC%20BY--NC%204.0-lightgrey.svg


task_categories:text-classification task_categories:text-generation task_ids:text-scoring task_ids:dialogue-modeling annotations_creators:crowdsourced language_creators:expert-generated language_creators:other multilinguality:monolingual size_categories:100K<n<1M size_categories:10K<n<100K source_datasets:original language:en license:cc-by-nc-4.0 arxiv:2004.09544 region:us
{"annotations_creators": ["crowdsourced"], "language_creators": ["expert-generated", "other"], "language": ["en"], "license": ["cc-by-nc-4.0"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M", "10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification", "text-generation"], "task_ids": ["text-scoring", "dialogue-modeling"], "pretty_name": "spolin"}
TAGS
#task_categories-text-classification #task_categories-text-generation #task_ids-text-scoring #task_ids-dialogue-modeling #annotations_creators-crowdsourced #language_creators-expert-generated #language_creators-other #multilinguality-monolingual #size_categories-100K<n<1M #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-nc-4.0 #arxiv-2004.09544 #region-us 

SPOLIN
======


[![CC BY-NC 4.0](URL)](URL)


Table of Contents
-----------------


* Dataset Description
	+ Dataset Summary
	+ Available SPOLIN Versions
	+ Relevant Links
* Dataset Structure
* Dataset Statistics
* Other Information
	+ ACL Presentation
	+ Licensing Information
	+ Citation Information


Dataset Description
-------------------


### Dataset Summary


This is the repo for the paper "Grounding Conversations with Improvised Dialogues" (ACL2020).
The *Selected Pairs of Learnable ImprovisatioN* (SPOLIN) corpus is a collection of more than 68,000 "Yes, and" type dialogue pairs extracted from the Spontaneanation podcast by Paul F. Tompkins, the Cornell Movie-Dialogs Corpus, and the SubTle corpus. For more information, refer to our paper or our project page.


### Available SPOLIN Versions:


The core dataset that was used for the experiments in the paper only includes *yes-ands* and non-*yes-ands* from Spontaneanation and most of what is provided in those extracted from the Cornell Movie-Dialogs Corpus. After the submitting the paper, we continued our iterative data augmentation process, repeating another iteration with the Cornell Movie-Dialogs Corpus and extracting from the SubTle corpus. This expanded version is also included in this repository here. This latest version of SPOLIN was used to train the model used in our demo.


In the 'data' folder, we provide two versions of the SPOLIN training set:


1. Version used for experiments in the ACL paper: 'data/URL'
2. Expanded version: 'data/URL'


### Relevant Links:


* Project page: URL
* Github repo: URL
* SpolinBot Demo: URL
* ACL2020 Paper: URL


Dataset Structure
-----------------


Fields


* 'id': unique identifier
* 'prompt': first utterance in utterance pair
* 'response': second utterance in utterance pair
* 'label': yesand = 1, non-yesand = 0
* 'source': the source for the sample
* 'split': whether the sample belongs to the training set or the validation set


Dataset Statistics
------------------


##### 'URL':



##### 'URL':



##### 'URL':



\*Artificially collected by mix & matching positive Spontaneanation samples to balance dataset for training classifier


Other Information
-----------------


### ACL Presentation


Video recording


If you use our data for your work, please cite our ACL2020 paper:


### Licensing Information


This work is licensed under a [Creative Commons Attribution-NonCommercial 4.0 International License](URL).


[![CC BY-NC 4.0](URL)](URL)



### Dataset Summary


This is the repo for the paper "Grounding Conversations with Improvised Dialogues" (ACL2020).
The *Selected Pairs of Learnable ImprovisatioN* (SPOLIN) corpus is a collection of more than 68,000 "Yes, and" type dialogue pairs extracted from the Spontaneanation podcast by Paul F. Tompkins, the Cornell Movie-Dialogs Corpus, and the SubTle corpus. For more information, refer to our paper or our project page. ### Available SPOLIN Versions:


The core dataset that was used for the experiments in the paper only includes *yes-ands* and non-*yes-ands* from Spontaneanation and most of what is provided in those extracted from the Cornell Movie-Dialogs Corpus. After the submitting the paper, we continued our iterative data augmentation process, repeating another iteration with the Cornell Movie-Dialogs Corpus and extracting from the SubTle corpus. This expanded version is also included in this repository here. This latest version of SPOLIN was used to train the model used in our demo.


In the 'data' folder, we provide two versions of the SPOLIN training set:


1. Version used for experiments in the ACL paper: 'data/URL'
2. Expanded version: 'data/URL' ### Relevant Links:


* Project page: URL
* Github repo: URL
* SpolinBot Demo: URL
* ACL2020 Paper: URL


Dataset Structure
-----------------


Fields


* 'id': unique identifier
* 'prompt': first utterance in utterance pair
* 'response': second utterance in utterance pair
* 'label': yesand = 1, non-yesand = 0
* 'source': the source for the sample
* 'split': whether the sample belongs to the training set or the validation set


Dataset Statistics
------------------ ##### 'URL': ##### 'URL': ##### 'URL':



\*Artificially collected by mix & matching positive Spontaneanation samples to balance dataset for training classifier


Other Information
----------------- ### ACL Presentation


Video recording


If you use our data for your work, please cite our ACL2020 paper: ### Licensing Information


This work is licensed under a [Creative Commons Attribution-NonCommercial 4.0 International License](URL).


[![CC BY-NC 4.0](URL)](URL)
TAGS
#task_categories-text-classification #task_categories-text-generation #task_ids-text-scoring #task_ids-dialogue-modeling #annotations_creators-crowdsourced #language_creators-expert-generated #language_creators-other #multilinguality-monolingual #size_categories-100K<n<1M #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-nc-4.0 #arxiv-2004.09544 #region-us 
 ### Dataset Summary


This is the repo for the paper "Grounding Conversations with Improvised Dialogues" (ACL2020).
The *Selected Pairs of Learnable ImprovisatioN* (SPOLIN) corpus is a collection of more than 68,000 "Yes, and" type dialogue pairs extracted from the Spontaneanation podcast by Paul F. Tompkins, the Cornell Movie-Dialogs Corpus, and the SubTle corpus. For more information, refer to our paper or our project page. ### Available SPOLIN Versions:


The core dataset that was used for the experiments in the paper only includes *yes-ands* and non-*yes-ands* from Spontaneanation and most of what is provided in those extracted from the Cornell Movie-Dialogs Corpus. After the submitting the paper, we continued our iterative data augmentation process, repeating another iteration with the Cornell Movie-Dialogs Corpus and extracting from the SubTle corpus. This expanded version is also included in this repository here. This latest version of SPOLIN was used to train the model used in our demo.


In the 'data' folder, we provide two versions of the SPOLIN training set:


1. Version used for experiments in the ACL paper: 'data/URL'
2. Expanded version: 'data/URL' ### Relevant Links:


* Project page: URL
* Github repo: URL
* SpolinBot Demo: URL
* ACL2020 Paper: URL


Dataset Structure
-----------------


Fields


* 'id': unique identifier
* 'prompt': first utterance in utterance pair
* 'response': second utterance in utterance pair
* 'label': yesand = 1, non-yesand = 0
* 'source': the source for the sample
* 'split': whether the sample belongs to the training set or the validation set


Dataset Statistics
------------------ ##### 'URL': ##### 'URL': ##### 'URL':



\*Artificially collected by mix & matching positive Spontaneanation samples to balance dataset for training classifier


Other Information
----------------- ### ACL Presentation


Video recording


If you use our data for your work, please cite our ACL2020 paper: ### Licensing Information


This work is licensed under a [Creative Commons Attribution-NonCommercial 4.0 International License](URL).


[![CC BY-NC 4.0](URL)](URL)