Datasets:
meta_woz

Languages: English
Multilinguality: monolingual
Size Categories: 10K<n<100K
Language Creators: crowdsourced
Annotations Creators: crowdsourced
Source Datasets: original
Licenses: other
meta_woz / dataset_infos.json
{"dialogues": {"description": "MetaLWOz: A Dataset of Multi-Domain Dialogues for the Fast Adaptation of Conversation Models. We introduce the Meta-Learning Wizard of Oz (MetaLWOz) dialogue dataset for developing fast adaptation methods for conversation models. This data can be used to train task-oriented dialogue models, specifically to develop methods to quickly simulate user responses with a small amount of data. Such fast-adaptation models fall into the research areas of transfer learning and meta learning. The dataset consists of 37,884 crowdsourced dialogues recorded between two human users in a Wizard of Oz setup, in which one was instructed to behave like a bot, and the other a true human user. The users are assigned a task belonging to a particular domain, for example booking a reservation at a particular restaurant, and work together to complete the task. Our dataset spans 47 domains having 227 tasks total. Dialogues are a minimum of 10 turns long.\n", "citation": "@InProceedings{shalyminov2020fast,\nauthor = {Shalyminov, Igor and Sordoni, Alessandro and Atkinson, Adam and Schulz, Hannes},\ntitle = {Fast Domain Adaptation For Goal-Oriented Dialogue Using A Hybrid Generative-Retrieval Transformer},\nbooktitle = {2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},\nyear = {2020},\nmonth = {April},\nurl = {https://www.microsoft.com/en-us/research/publication/fast-domain-adaptation-for-goal-oriented-dialogue-using-a-hybrid-generative-retrieval-transformer/},\n}\n", "homepage": "https://www.microsoft.com/en-us/research/project/metalwoz/", "license": "Microsoft Research Data License Agreement", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "user_id": {"dtype": "string", "id": null, "_type": "Value"}, "bot_id": {"dtype": "string", "id": null, "_type": "Value"}, "domain": {"dtype": "string", "id": null, "_type": "Value"}, "task_id": {"dtype": "string", "id": null, "_type": "Value"}, "turns": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "meta_woz", "config_name": "dialogues", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 19999218, "num_examples": 37884, "dataset_name": "meta_woz"}, "test": {"name": "test", "num_bytes": 1284287, "num_examples": 2319, "dataset_name": "meta_woz"}}, "download_checksums": {"https://download.microsoft.com/download/E/B/8/EB84CB1A-D57D-455F-B905-3ABDE80404E5/metalwoz-v1.zip": {"num_bytes": 5639228, "checksum": "2a2ae3b25760aa2725e70bc6480562fa5d720c9689a508d28417631496d6764f"}, "https://download.microsoft.com/download/0/c/4/0c4a8893-cbf9-4a43-a44a-09bab9539234/metalwoz-test-v1.zip": {"num_bytes": 2990635, "checksum": "6722d1d9ec05334dd801972767ae3bdefcd15f71bf73fea1d098f214a96a7c6c"}}, "download_size": 8629863, "post_processing_size": null, "dataset_size": 21283505, "size_in_bytes": 29913368}, "tasks": {"description": "MetaLWOz: A Dataset of Multi-Domain Dialogues for the Fast Adaptation of Conversation Models. We introduce the Meta-Learning Wizard of Oz (MetaLWOz) dialogue dataset for developing fast adaptation methods for conversation models. This data can be used to train task-oriented dialogue models, specifically to develop methods to quickly simulate user responses with a small amount of data. Such fast-adaptation models fall into the research areas of transfer learning and meta learning. The dataset consists of 37,884 crowdsourced dialogues recorded between two human users in a Wizard of Oz setup, in which one was instructed to behave like a bot, and the other a true human user. The users are assigned a task belonging to a particular domain, for example booking a reservation at a particular restaurant, and work together to complete the task. Our dataset spans 47 domains having 227 tasks total. Dialogues are a minimum of 10 turns long.\n", "citation": "@InProceedings{shalyminov2020fast,\nauthor = {Shalyminov, Igor and Sordoni, Alessandro and Atkinson, Adam and Schulz, Hannes},\ntitle = {Fast Domain Adaptation For Goal-Oriented Dialogue Using A Hybrid Generative-Retrieval Transformer},\nbooktitle = {2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},\nyear = {2020},\nmonth = {April},\nurl = {https://www.microsoft.com/en-us/research/publication/fast-domain-adaptation-for-goal-oriented-dialogue-using-a-hybrid-generative-retrieval-transformer/},\n}\n", "homepage": "https://www.microsoft.com/en-us/research/project/metalwoz/", "license": "Microsoft Research Data License Agreement", "features": {"task_id": {"dtype": "string", "id": null, "_type": "Value"}, "domain": {"dtype": "string", "id": null, "_type": "Value"}, "bot_prompt": {"dtype": "string", "id": null, "_type": "Value"}, "bot_role": {"dtype": "string", "id": null, "_type": "Value"}, "user_prompt": {"dtype": "string", "id": null, "_type": "Value"}, "user_role": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "meta_woz", "config_name": "tasks", "version": "0.0.0", "splits": {"train": {"name": "train", "num_bytes": 73768, "num_examples": 227, "dataset_name": "meta_woz"}, "test": {"name": "test", "num_bytes": 4351, "num_examples": 14, "dataset_name": "meta_woz"}}, "download_checksums": {"https://download.microsoft.com/download/E/B/8/EB84CB1A-D57D-455F-B905-3ABDE80404E5/metalwoz-v1.zip": {"num_bytes": 5639228, "checksum": "2a2ae3b25760aa2725e70bc6480562fa5d720c9689a508d28417631496d6764f"}, "https://download.microsoft.com/download/0/c/4/0c4a8893-cbf9-4a43-a44a-09bab9539234/metalwoz-test-v1.zip": {"num_bytes": 2990635, "checksum": "6722d1d9ec05334dd801972767ae3bdefcd15f71bf73fea1d098f214a96a7c6c"}}, "download_size": 8629863, "post_processing_size": null, "dataset_size": 78119, "size_in_bytes": 8707982}}