Task-Oriented Dialogue Dataset Survey
This repo is a dataset survey for Task-oriented Dialogue.
We investigated most existing dialogue datasets and summarize their basic information, such download link and size.
We also include leader boards of some dataset to present research progress in the task oriented dialogue fields.
A Chinese intro & news for this project is available here
Contributions are welcomed, you are encouraged to:
- Directly pull request
- Send me new dataset info
- Send me new experiment results from published paper.
The ranking is depended on published results of related papers. We are trying to keep it up-to-date. The ranking may be unfair because features used and train/dev set splitting in those papers may be different. However it shows a trend of research, and would be helpful for someone to start a project about task-oriented dialogue.
NLU: Slot Filling
Slot filling task aims to recognize key entity of user utterance, such position and time.
|Model||F1||Paper / Source|
|Enc-dec (focus) + BERT||97.17||Code|
|Joint BERT (Chen et al., 2019)||97.0||BERT for Joint Intent Classification and Slot Filling|
|BLSTM-CRF + ELMo word embedding||96.92||Code|
|ELMo + BLSTM-CRF (Siddhant et al., 2018)||93.90||Unsupervised Transfer Learning for Spoken Language Understanding in Intelligent Agents|
|Capsule Neural Networks (Zhang et al., 2018)||91.8||Joint Slot Filling and Intent Detection via Capsule Neural Networks|
|Slot-Gated (Full Atten.) (Goo et al., 2018)||88.8||Slot-Gated Modeling for Joint Slot Filling and Intent Prediction|
|BLSTM-CRF (Siddhant et al., 2018)||88.78||Unsupervised Transfer Learning for Spoken Language Understanding in Intelligent Agents|
|Slot-Gated (Intent Atten.) (Goo et al., 2018)||88.3||Slot-Gated Modeling for Joint Slot Filling and Intent Prediction|
NLU: Intent Detection
Slot filling task aims to classify user utterance into different domain.
|Model||Acc.||Paper / Source|
|ELMo + BLSTM-CRF (Siddhant et al., 2018)||99.29||Unsupervised Transfer Learning for Spoken Language Understanding in Intelligent Agents|
|Enc-dec (focus) + ELMo||99.14||Code|
|Joint BERT (Chen et al., 2019)||98.6||BERT for Joint Intent Classification and Slot Filling|
|Capsule Neural Networks (Zhang et al., 2018)||97.7||Joint Slot Filling and Intent Detection via Capsule Neural Networks|
|Slot-Gated (Full Atten.) (Goo et al., 2018)||97.0||Slot-Gated Modeling for Joint Slot Filling and Intent Prediction|
|Slot-Gated (Intent Atten.) (Goo et al., 2018)||96.8||Slot-Gated Modeling for Joint Slot Filling and Intent Prediction|
Dialogue State Tracking
Dialogue state tacking task aims to predict or give representation of dialogue state, which usually contains a goal constraint, a set of requested slots, and the user's dialogue act.
Clarification of dataset types:
The main results we list here are obtained from pure DSTC2 dataset (ASR n-best).
However, we don't list other kinds of DSTC2 data source results such as DSTC2-text (It formulates the dialog state tracking as a machine reading problem which read the dialog transcriptions multiple times and answer the questions about each of the slot, for more info please refer to paper) and DSTC-cleaned (It is used by the NBT paper and fixes ASR noise and typo during training and include ASR noise during testing, The cleaned version is available at here),
|Model||Area||Food||Price||Joint||Paper / Source|
|Liu et al. (2018)||90||84||92||72||Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems|
|Neural belief tracker (Mrkšić et al., 2017)||90||84||94||72||Neural Belief Tracker: Data-Driven Dialogue State Tracking|
|RNN (Henderson et al., 2014)||92||86||86||69||Robust dialog state tracking using delexicalised recurrent neural networks and unsupervised gate|
See the data details Here or in Excel File
Following information is included for each dataset:
- Link (Download & Paper)
- Multi or single turn
- Task detail
- Whether Public Accessible
- Size & Stats
- Included Label
- Missing Label
|Name||Introduction||Links||Multi/Single Turn||Task||Task Detail||Public Accessible||Size & Stats||Included Label||Missing Label|
|MultiWOZ 2.0||1. Proposed by EMNLP 2018 best paper. 2. Largest by now & contain multi-domains. 3. Human2human 4. goal changes are encouraged||Download: http://dialogue.mi.eng.cam.ac.uk/index.php/corpus/ Paper: https://arxiv.org/pdf/1810.00278.pdf||M||Task Oriented||7 domains Attraction, Hospital, Police, Hotel, Restaurant, Taxi, Train.||Yes||Total 10438 dialogues average number of turns are 8.93 and 15.39 for single and multi-domain dialogues respectively. 115, 434 turns in total.||Belief state User Act(inform, request slots) Agent Act(inform, request slots)||NLU(Intent, Slots)|
|Medical DS||1. Our dataset is collected from the pediatric department in a Chinese online healthcare community 2. Task-oriented Dialogue System for Automatic Diagnosis||Download: http://www.sdspeople.fudan.edu.cn/zywei/data/acl2018-mds.zip Paper: http://www.sdspeople.fudan.edu.cn/zywei/paper/liu-acl2018.pdf||M||Task Oriented||Automatic Diagnosis||Yes||4 Disease 67 symptoms||Slot Action|
|Snips||1. Collected by Snips for model evaluation. 2. For natural language understanding 3. Homepage: https://medium.com/snips-ai/benchmarking-natural-language-understanding-systems-google-facebook-microsoft-and-snips-2b8ddcf9fb19||Download: https://github.com/snipsco/ nlu-benchmark/tree/master/ 2017-06-custom-intent-engines||S||Task Oriented||7 task: Weather,play music, search, add to list, book, moive||Yes||Train:13,084 Test:700 7 intent 72 slot labels||Intent Slots|
|MIT Restaurant Corpus||1. The MIT Restaurant Corpus is a semantically tagged training and test corpus in BIO format. 2. For natural language understanding||Download: https://groups.csail.mit.edu/sls/downloads/restaurant/||S||Task Oriented||Resaurant||Yes||Train, Dev, Test 6,894 766 1,521||Slot||Intent|
|MIT Movie Corpus||1. The MIT Movie Corpus is a semantically tagged training and test corpus in BIO format. The eng corpus are simple queries, and the trivia10k13 corpus are more complex queries. 2. For natural language understanding||Download: https://groups.csail.mit.edu/sls/downloads/movie/||S||Task Oriented||Movie||Yes||Train, Dev, Test MIT Movie Eng 8,798 977 2,443 MIT Movie Trivia 7,035 781 1,953 Refer to: Data Augmentation for Spoken Language Understanding via Joint Variational Generation||Slot||Intent|
|ATIS||1. The ATIS (Airline Travel Information Systems) dataset (Tur et al., 2010) is widely used in SLU research 2. For natural language understanding||Download: 1. https://github.com/AtmaHou/Bi-LSTM_PosTagger/tree/master/data 2.https://github.com/yvchen/JointSLU/tree/master/data||S||Task Oriented||Airline Travel Information||Yes||Train: 4478 Test: 893 120 slot and 21 intent||Intent Slots|
|Microsoft Dialogue Challenge||1. Containing human-annotated conversational data in three domains an 2. Experiment platform with built-in simulators in each domain, for training and evaluation purposes.||Download: https://github.com/xiul-msr/e2e_dialog_challenge/tree/master/data Paper： https://arxiv.org/pdf/1807.11125.pdf||M||Task Oriented||Movie-Ticket Booking Restaurant Reservation Taxi Ordering||Yes||Task Intents Slots Dialogues Movie-Ticket Booking 11 29 2890 Restaurant Reservation 11 30 4103 Taxi Ordering 11 29 3094||Intent Slots||Database API-call|
|CamRest676||CamRest676 Human2Human dataset contains the following three json files: 1. CamRest676.json: the woz dialogue dataset, which contains the conversion from users and wizards, as well as a set of coarse labels for each user turn. 2. CamRestDB.json: the Cambridge restaurant database file, containing restaurants in the Cambridge UK area and a set of attributes. 3. The ontology file, specific all the values the three informable slots can take.||Download: https://www.repository.cam.ac.uk/handle/1810/260970 Paper: https://arxiv.org/abs/1604.04562||M||Task Oriented||Booking restaurant||Yes||Total 676 Dialogues Total 1500 Turns Train:Dev:Test 3:1:1 (Test set not given)||Slot User Act(inform, request slots) Agent Act(inform, request slots)||Intent API call Database|
|Frames||1. Maluuba reased a travel booking dataset 2. Design for new task: frame tracking (allow comparing between history entities) 3. Homepage: https://datasets.maluuba.com/Frames 4. Human2Human||Download: https://datasets.maluuba.com/Frames/dl Paper: https://arxiv.org/abs/1706.01690 https://1drv.ms/b/s!Aqj1OvgfsHB7dsg42yp2BzDUK6U||M||Task Oriented||Travel Booking||Yes||Dialogues 1369 Turns 19986 Average user satisfaction (from 1-5) 4.58||Frame User agenda User Act(inform, request slots) Agent Act(inform, request slots) API Call User's satisfaction Task successful Database Entity reference||Intent|
|Dialog bAbI tasks data||1. Facebook's 6 task-oriented dialogues data set consist of 6 different tasks. 2. Dataset for task 1-5 is constucted automaticly from bots' chat(Bot2Bot). And dataset for task 6 is simply reformated dstc2 dataset. 3. A Shared database is included. 4. This is the only task-oriented dataset among bAbI tasks. 5. The goal of it is to evaluate end2end tasks, so there is not intents and slots.||Download: https://research.fb.com/downloads/babi/ Paper: http://arxiv.org/abs/1605.07683||M||Task Oriented||Book a table at a restaurant||Yes||For each task, training 1000 develop 1000 test 1000 For tasks 1-5, second test set (with suffix -OOV.txt) that contains dialogs including entities not present.||API call Full Database||Slot Intent User Act Agent Act|
|Stanford Dialog Dataset||1. Standford NLP group's data of car autopilot agent. 2. Human2Human 3. A quick intro http://m.sohu.com/n/499803391/||Download: http://nlp.stanford.edu/projects/kvret/kvret_dataset_public.zip Paper: https://arxiv.org/abs/1705.05414||M||Task Oriented||car autopilot agent: schedule, weather, navigation||Yes||Training Dialogues 2,425 Validation Dialogues 302 Test Dialogues 304 Avg. # of Utterances Per Dialogue 5.25||Dialogue level database User Act(inform, request slots) Agent Act(inform, request slots)||API call Intent Slot|
|Stanford Dialog Dataset LU||1. Stanford data labeled by HIT, relabel slot & intent 2. Human2Human 3. A quick intro http://m.sohu.com/n/499803391/ to stanford data 4. Annotation handbook: https://docs.google.com/document/d/1ROARKf8AJNnG2_nPINe1Xm5Rza7V0jPnQV8io09hcFY/edit||N/A||M||Task Oriented||car autopilot agent: schedule, weather, navigation||No||Training Dialogues 2,425 Validation Dialogues 302 Test Dialogues 304 Avg. # of Utterances Per Dialogue 5.25||Slot Intent||API call Need to do sample alignment to get the following: Dialogue level database User Act(inform, request slots) Agent Act(inform, request slots) Agent Reply|
|DSTC-2||1. Human2Bot restaurant booking dataset 2. For usage refer to: http://camdial.org/~mh521/dstc/downloads/handbook.pdf 3. Each dialofue is stored in different folder, which contains log and label.||http://camdial.org/~mh521/dstc/||M||Task Oriented||Booking restaurant||Yes||Train 1612 calls Dev 506 calls Test 1117 dialogs||Slot User Act(inform, request slots) Agent Act(inform, request slots)||Intent API call Database|
|DSTC4||1. Data name as TourSG consists of 35 dialog sessions on touristic information for Singapore collected from Skype calls between three tour guides and 35 tourists 2. All the recorded dialogs with the total length of 21 hours have been manually transcribed and annotated with speech act and semantic labels for each turn level. 3. Homepage: http://www.colips.org/workshop/dstc4/data.html 4. Human2Human||N/A||M||Task Oriented||Query touristic information||No||Train 20 dialogs Test 15 dialogs||speech act (User & Agent) semantic labels(Intent? User & Agent) topic for turn (Intent?)||N/A|
|Movie Booking Dataset||1. (Microsoft) Raw conversational data collected via Amazon Mechanical Turk, with annotations provided by domain experts. 2. Human2Human||Download: https://github.com/MiuLab/TC-Bot#data Paper: TC-bot||M||Task Oriented||Booking Movie||Yes||280 dialogues turns per dialogue is approximately 11||User Act(inform, request slots) Agent Act(inform, request slots) Intent Slots||Database API-call|
|Lingxi||1. The data is all single round user input divided into good words. There is more noise. 2. Completed part of speech tagging and slot labeling 3. Language: Chinese||N/A||S||Task Oriented||conversational robot service user log||No||Utterance: 5132||Slot POS||Agent reply Intent API call Database|
|TOP semantic parsing||1. (Facebook) A hierarchical semantic representation for task oriented dialog systems that can model compositional and nested queries. (hierarchical intent and slot) 2. For natural language understanding 3. Human2bot||Download: http://fb.me/semanticparsingdialog ; Paper: https://arxiv.org/pdf/1810.07942.pdf||S||Task Oriented||Navigation and event||Yes||Train 31279 utterances; Dev 4462 utterances; Test 9042 utterances||Hierarchical intents; Slots|
|Facebook Multilingual Task Oriented Dataset||1. (Faceboook) We release a dataset of around 57k annotated utterances in English (43k), Spanish (8.6k) and Thai (5k) for three task oriented domains … ALARM, REMINDER, and WEATHER. 2. For cross-lingual natural language understanding||Download: https://fb.me/multilingual_task_oriented_data Paper: https://arxiv.org/pdf/1810.13327.pdf||S||Task Oriented||3 Domains: Alarm, Reminder, Weather and 3 Languages: English, Spanish, Thai||Yes||English Train: 30,521; English Dev: 4,181; English Test: 8,621; Spanish Train: 3,617; Spanish Dev: 1,983; Spanish Test: 3,043; Thai Train: 2,156; Thai Dev: 1,235; Thai Test: 1,692||Slot Intent|
Thanks for supports from my adviser Wanxiang Che.
Thanks for public contributions from: