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https://github.com/awslabs/multi-domain-goal-oriented-dialogues-dataset

Data from the publication "Multi-Domain Goal-Oriented Dialogues (MultiDoGO): Strategies toward Curating and Annotating Large Scale Dialogue Data"

https://github.com/awslabs/multi-domain-goal-oriented-dialogues-dataset

Science Score: 13.0%

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    Found 1 DOI reference(s) in README
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Repository

Data from the publication "Multi-Domain Goal-Oriented Dialogues (MultiDoGO): Strategies toward Curating and Annotating Large Scale Dialogue Data"

Basic Info
  • Host: GitHub
  • Owner: awslabs
  • License: other
  • Default Branch: master
  • Homepage:
  • Size: 56.4 MB
Statistics
  • Stars: 21
  • Watchers: 5
  • Forks: 3
  • Open Issues: 3
  • Releases: 0
Created about 6 years ago · Last pushed over 5 years ago
Metadata Files
Readme Contributing License Code of conduct

README.md

Data from "Multi-Domain Goal-Oriented Dialogues (MultiDoGO): Strategies toward Curating and Annotating Large Scale Dialogue Data"

Repository Structure

Under the top level ./data directory, you will find the following two sub-directories:

1. unannotated:

unannotated human to human conversations from the airline, fastfood, finance, insurance, media, and software domains. Conversations are split by domain and given in TSV format with columns: "conversationId", "turnNumber", "utteranceId", "utterance", "authorRole".

2. paper_splits:

pre-processed training, development, and test splits for customer turns used to obtain intent classification and slot-labeling results in Table 7 of the paper. As in the paper, we partition these data by annotation granularity, either sentence level (located at ./data/papersplits/splitsannotatedatsentencelevel) or turn level (located at ./data/papersplits/splitsannotatedatturnlevel). Under each annotation granularity subdirectory, we provide splits for each domain: airline, fastfood, finance, insurance, media, and software. The splits are labeled as "train.tsv", "dev.tsv", "test.tsv" and contain the following tab separated columns: "conversationId", "turnNumber", "sentenceNumber" (only for sentence level splits), "utteranceId", "utterance", "slot-labels", and "intent". The labels in the slot-labels field are separated by spaces. In the case of multiple intents for a single input, we separate the intents with the special token <div>.

License

This project is licensed under the CDLA Permissive License. Terms given in LICENSE.txt.

Reference

For reference please cite our EMNLP-2019 paper: Multi-Domain Goal-Oriented Dialogues (MultiDoGO): Strategies toward Curating and Annotating Large Scale Dialogue Data (BibTex below)

@inproceedings{peskov-etal-2019-multi, title = "Multi-Domain Goal-Oriented Dialogues ({M}ulti{D}o{GO}): Strategies toward Curating and Annotating Large Scale Dialogue Data", author = "Peskov, Denis and Clarke, Nancy and Krone, Jason and Fodor, Brigi and Zhang, Yi and Youssef, Adel and Diab, Mona", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)", year = "2019", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D19-1460", doi = "10.18653/v1/D19-1460", pages = "4526--4536", }

Owner

  • Name: Amazon Web Services - Labs
  • Login: awslabs
  • Kind: organization
  • Location: Seattle, WA

AWS Labs

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