
# Dataset Card for d0rj/conv_ai_3_ru

## Dataset Description

- **Homepage:** https://github.com/aliannejadi/ClariQ
- **Repository:** https://github.com/aliannejadi/ClariQ
- **Paper:** https://arxiv.org/abs/2009.11352

### Dataset Summary

This is translated version of [conv_ai_3](https://huggingface.co/datasets/conv_ai_3) dataset to Russian language.

### Languages

Russian (translated from English).

## Dataset Structure

### Data Fields

- `topic_id`: the ID of the topic (`initial_request`).
- `initial_request`: the query (text) that initiates the conversation.
- `topic_desc`: a full description of the topic as it appears in the TREC Web Track data.
- `clarification_need`: a label from 1 to 4, indicating how much it is needed to clarify a topic. If an `initial_request` is self-contained and would not need any clarification, the label would be 1. While if a `initial_request` is absolutely ambiguous, making it impossible for a search engine to guess the user's right intent before clarification, the label would be 4.
- `facet_id`: the ID of the facet.
- `facet_desc`: a full description of the facet (information need) as it appears in the TREC Web Track data.
- `question_id`: the ID of the question..
- `question`: a clarifying question that the system can pose to the user for the current topic and facet.
- `answer`: an answer to the clarifying question, assuming that the user is in the context of the current row (i.e., the user's initial query is `initial_request`, their information need is `facet_desc`, and `question` has been posed to the user).

### Citation Information

@misc{aliannejadi2020convai3,
title={ConvAI3: Generating Clarifying Questions for Open-Domain Dialogue Systems (ClariQ)},
author={Mohammad Aliannejadi and Julia Kiseleva and Aleksandr Chuklin and Jeff Dalton and Mikhail Burtsev},
year={2020},
eprint={2009.11352},
archivePrefix={arXiv},
primaryClass={cs.CL}
}

### Contributions

Thanks to [@rkc007](https://github.com/rkc007) for adding this dataset.
task_categories:conversational task_categories:text-classification task_ids:text-scoring annotations_creators:crowdsourced language_creators:translated multilinguality:monolingual size_categories:10K<n<100K source_datasets:conv_ai_3 language:ru license:unknown evaluating-dialogue-systems arxiv:2009.11352 region:us
{"annotations_creators": ["crowdsourced"], "language_creators": ["translated"], "language": ["ru"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["conv_ai_3"], "task_categories": ["conversational", "text-classification"], "task_ids": ["text-scoring"], "pretty_name": "conv_ai_3 (ru)", "tags": ["evaluating-dialogue-systems"], "dataset_info": {"features": [{"name": "topic_id", "dtype": "int32"}, {"name": "initial_request", "dtype": "string"}, {"name": "topic_desc", "dtype": "string"}, {"name": "clarification_need", "dtype": "int32"}, {"name": "facet_id", "dtype": "string"}, {"name": "facet_desc", "dtype": "string"}, {"name": "question_id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "config_name": "conv_ai_3", "splits": [{"name": "train", "num_examples": 9176}, {"name": "validation", "num_examples": 2313}]}}
TAGS
#task_categories-conversational #task_categories-text-classification #task_ids-text-scoring #annotations_creators-crowdsourced #language_creators-translated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-conv_ai_3 #language-Russian #license-unknown #evaluating-dialogue-systems #arxiv-2009.11352 #region-us 


# Dataset Card for d0rj/conv_ai_3_ru

## Dataset Description

- Homepage: URL
- Repository: URL
- Paper: URL

### Dataset Summary

This is translated version of conv_ai_3 dataset to Russian language.

### Languages

Russian (translated from English).

## Dataset Structure

### Data Fields

- 'topic_id': the ID of the topic ('initial_request').
- 'initial_request': the query (text) that initiates the conversation.
- 'topic_desc': a full description of the topic as it appears in the TREC Web Track data.
- 'clarification_need': a label from 1 to 4, indicating how much it is needed to clarify a topic. If an 'initial_request' is self-contained and would not need any clarification, the label would be 1. While if a 'initial_request' is absolutely ambiguous, making it impossible for a search engine to guess the user's right intent before clarification, the label would be 4.
- 'facet_id': the ID of the facet.
- 'facet_desc': a full description of the facet (information need) as it appears in the TREC Web Track data.
- 'question_id': the ID of the question..
- 'question': a clarifying question that the system can pose to the user for the current topic and facet.
- 'answer': an answer to the clarifying question, assuming that the user is in the context of the current row (i.e., the user's initial query is 'initial_request', their information need is 'facet_desc', and 'question' has been posed to the user).



@misc{aliannejadi2020convai3,
title={ConvAI3: Generating Clarifying Questions for Open-Domain Dialogue Systems (ClariQ)},
author={Mohammad Aliannejadi and Julia Kiseleva and Aleksandr Chuklin and Jeff Dalton and Mikhail Burtsev},
year={2020},
eprint={2009.11352},
archivePrefix={arXiv},
primaryClass={cs.CL}
}

### Contributions

Thanks to @rkc007 for adding this dataset.
# Dataset Card for d0rj/conv_ai_3_ru ## Dataset Description

- Homepage: URL
- Repository: URL
- Paper: URL ### Dataset Summary

This is translated version of conv_ai_3 dataset to Russian language. ### Languages

Russian (translated from English). ## Dataset Structure ### Data Fields

- 'topic_id': the ID of the topic ('initial_request').
- 'initial_request': the query (text) that initiates the conversation.
- 'topic_desc': a full description of the topic as it appears in the TREC Web Track data.
- 'clarification_need': a label from 1 to 4, indicating how much it is needed to clarify a topic. If an 'initial_request' is self-contained and would not need any clarification, the label would be 1. While if a 'initial_request' is absolutely ambiguous, making it impossible for a search engine to guess the user's right intent before clarification, the label would be 4.
- 'facet_id': the ID of the facet.
- 'facet_desc': a full description of the facet (information need) as it appears in the TREC Web Track data.
- 'question_id': the ID of the question..
- 'question': a clarifying question that the system can pose to the user for the current topic and facet.
- 'answer': an answer to the clarifying question, assuming that the user is in the context of the current row (i.e., the user's initial query is 'initial_request', their information need is 'facet_desc', and 'question' has been posed to the user).



@misc{aliannejadi2020convai3,
title={ConvAI3: Generating Clarifying Questions for Open-Domain Dialogue Systems (ClariQ)},
author={Mohammad Aliannejadi and Julia Kiseleva and Aleksandr Chuklin and Jeff Dalton and Mikhail Burtsev},
year={2020},
eprint={2009.11352},
archivePrefix={arXiv},
primaryClass={cs.CL}
} ### Contributions

Thanks to @rkc007 for adding this dataset.
TAGS
#task_categories-conversational #task_categories-text-classification #task_ids-text-scoring #annotations_creators-crowdsourced #language_creators-translated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-conv_ai_3 #language-Russian #license-unknown #evaluating-dialogue-systems #arxiv-2009.11352 #region-us 
 # Dataset Card for d0rj/conv_ai_3_ru ## Dataset Description

- Homepage: URL
- Repository: URL
- Paper: URL ### Dataset Summary

This is translated version of conv_ai_3 dataset to Russian language. ### Languages

Russian (translated from English). ## Dataset Structure ### Data Fields

- 'topic_id': the ID of the topic ('initial_request').
- 'initial_request': the query (text) that initiates the conversation.
- 'topic_desc': a full description of the topic as it appears in the TREC Web Track data.
- 'clarification_need': a label from 1 to 4, indicating how much it is needed to clarify a topic. If an 'initial_request' is self-contained and would not need any clarification, the label would be 1. While if a 'initial_request' is absolutely ambiguous, making it impossible for a search engine to guess the user's right intent before clarification, the label would be 4.
- 'facet_id': the ID of the facet.
- 'facet_desc': a full description of the facet (information need) as it appears in the TREC Web Track data.
- 'question_id': the ID of the question..
- 'question': a clarifying question that the system can pose to the user for the current topic and facet.
- 'answer': an answer to the clarifying question, assuming that the user is in the context of the current row (i.e., the user's initial query is 'initial_request', their information need is 'facet_desc', and 'question' has been posed to the user).



@misc{aliannejadi2020convai3,
title={ConvAI3: Generating Clarifying Questions for Open-Domain Dialogue Systems (ClariQ)},
author={Mohammad Aliannejadi and Julia Kiseleva and Aleksandr Chuklin and Jeff Dalton and Mikhail Burtsev},
year={2020},
eprint={2009.11352},
archivePrefix={arXiv},
primaryClass={cs.CL}
} ### Contributions

Thanks to @rkc007 for adding this dataset.