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A dataset survey about task-oriented dialogue, including recent datasets and SoA results & papers.
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readme.md

Task-Oriented Dialogue Dataset Survey

Content

Introduction

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

Call for Contributions

Contributions are welcomed, you are encouraged to:

  • Directly pull request
  • Send me new dataset info
  • Send me new experiment results from published paper.

Leader Boards

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.

ATIS

Model F1 Paper / Source
Bi-model with a decoder (Wang et al., 2018) 96.89 A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling
Intent Gating & self-attention (Li et al., 2018) 96.52 A Self-Attentive Model with Gate Mechanism for Spoken Language Understanding
Joint BERT (Chen et al., 2019) 96.1 BERT for Joint Intent Classification and Slot Filling
Atomic concept (Su Zhu and Kai Yu, 2018) 96.08 Concept Transfer Learning for Adaptive Language Understanding
Atten.-Base+Delexicalization (Shin et al., 2018) 96.08 Slot Filling with Delexicalized Sentence Generation
Atten.-Based (Liu and Lane, 2016) 95.98 Attention-based recurrent neural network models for joint intent detection and slot fillin
Encoder-decoder-pointer (Zhai et al., 2017) 95.86 Neural Models for Sequence Chunking
ELMo + BLSTM-CRF (Siddhant et al., 2018) 95.62 Unsupervised Transfer Learning for Spoken Language Understanding in Intelligent Agents
Capsule Neural Networks (Zhang et al., 2018) 95.2 Joint Slot Filling and Intent Detection via Capsule Neural Networks
Slot-Gated (Intent Atten.) (Goo et al., 2018) 95.2 Slot-Gated Modeling for Joint Slot Filling and Intent Prediction
Slot-Gated (Full Atten.) (Goo et al., 2018) 94.8 Slot-Gated Modeling for Joint Slot Filling and Intent Prediction

Snips

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.

ATIS

Model Acc. Paper / Source
BLSTM + BERT 99.10 Code
Bi-model with a decoder (Wang et al., 2018) 98.99 A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling
Intent Gating & self-attention (Li et al., 2018) 98.77 A Self-Attentive Model with Gate Mechanism for Spoken Language Understanding
Atten.-Based (Liu and Lane, 2016) 98.43 Attention-based recurrent neural network models for joint intent detection and slot filling
BLSTM (Zhang et al., 2016) 98.10 A Joint Model of Intent Determination and Slot Filling for Spoken Language Understanding
Joint BERT (Chen et al., 2019) 97.9 BERT for Joint Intent Classification and Slot Filling
Capsule Neural Networks (Zhang et al., 2018) 95.0 Joint Slot Filling and Intent Detection via Capsule Neural Networks
Slot-Gated (Intent Atten.) (Goo et al., 2018) 94.1 Slot-Gated Modeling for Joint Slot Filling and Intent Prediction
Slot-Gated (Full Atten.) (Goo et al., 2018) 93.6 Slot-Gated Modeling for Joint Slot Filling and Intent Prediction

Snips

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.

DSTC2

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

Dataset Introductions

See the data details Here or in Excel File

Following information is included for each dataset:

  • Name
  • Introduction
  • Link (Download & Paper)
  • Multi or single turn
  • Task
  • 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

Acknowledgment

Thanks for supports from my adviser Wanxiang Che.

Thanks for public contributions from:

JiAnge, Su Zhu, seeledu, Tony Lin, Jason Krone, .

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