Datasets:
Tasks:
Text Classification
Formats:
parquet
Sub-tasks:
text-scoring
Languages:
Russian
Size:
10K - 100K
ArXiv:
License:
Create README.md
Browse files
README.md
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---
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dataset_info:
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features:
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- name: topic_id
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dtype: string
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- name: answer
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dtype: string
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splits:
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- name: train
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num_bytes: 4327400
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num_examples: 9176
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- name: validation
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num_bytes: 1066112
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num_examples: 2313
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download_size: 581860
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dataset_size: 5393512
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---
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# Dataset Card for "conv_ai_3_ru"
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-
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---
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annotations_creators:
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- crowdsourced
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language_creators:
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- translated
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language:
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- ru
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license:
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- unknown
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multilinguality:
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- monolingual
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size_categories:
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- 10K<n<100K
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source_datasets:
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- conv_ai_3
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task_categories:
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- conversational
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- text-classification
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task_ids:
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- text-scoring
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paperswithcode_id: null
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pretty_name: conv_ai_3 (ru)
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tags:
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- evaluating-dialogue-systems
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dataset_info:
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features:
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- name: topic_id
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dtype: string
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- name: answer
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dtype: string
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config_name: conv_ai_3
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splits:
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- name: train
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num_examples: 9176
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- name: validation
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num_examples: 2313
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---
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# Dataset Card for d0rj/conv_ai_3_ru
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## Dataset Description
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- **Homepage:** https://github.com/aliannejadi/ClariQ
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- **Repository:** https://github.com/aliannejadi/ClariQ
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- **Paper:** https://arxiv.org/abs/2009.11352
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### Dataset Summary
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This is translated version of [conv_ai_3](https://huggingface.co/datasets/conv_ai_3) dataset to Russian language.
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### Languages
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Russian (translated from English).
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## Dataset Structure
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### Data Fields
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- `topic_id`: the ID of the topic (`initial_request`).
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- `initial_request`: the query (text) that initiates the conversation.
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- `topic_desc`: a full description of the topic as it appears in the TREC Web Track data.
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- `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.
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- `facet_id`: the ID of the facet.
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- `facet_desc`: a full description of the facet (information need) as it appears in the TREC Web Track data.
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- `question_id`: the ID of the question..
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- `question`: a clarifying question that the system can pose to the user for the current topic and facet.
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- `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).
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### Citation Information
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@misc{aliannejadi2020convai3,
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title={ConvAI3: Generating Clarifying Questions for Open-Domain Dialogue Systems (ClariQ)},
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author={Mohammad Aliannejadi and Julia Kiseleva and Aleksandr Chuklin and Jeff Dalton and Mikhail Burtsev},
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year={2020},
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eprint={2009.11352},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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### Contributions
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Thanks to [@rkc007](https://github.com/rkc007) for adding this dataset.
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