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Datasets:
d0rj
/
conv_ai_3_ru

Tasks:
Text Classification
Sub-tasks:
text-scoring
Languages:
Russian
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
translated
Annotations Creators:
crowdsourced
Source Datasets:
conv_ai_3
ArXiv:
Tags:
evaluating-dialogue-systems
License:
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metadata
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
paperswithcode_id: null
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

Dataset Card for d0rj/conv_ai_3_ru

Dataset Description

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).

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 for adding this dataset.