Datasets:
Languages:
English
Multilinguality:
monolingual
Size Categories:
100K<n<1M
Language Creators:
machine-generated
Annotations Creators:
crowdsourced
Source Datasets:
original
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action
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"agent: Hello. How may I help you?",
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"customer: HI.",
"agent: Hello. How may I be of your address?",
"customer: I want to book a flight ticket to Las Vegas with price under 1000. Can you please help me with it?",
"agent: Sure, can I know your connection limit?",
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"customer: Thank you, my travelling dates are Apr 3rd and Apr 5th.",
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"customer: Hi. Donna Evans here.",
"agent: Hello, how can I help you today?",
"customer: I would like to prefer a flight with lay-by option for my business trip to Oakland. Can you book a flight ticket?",
"agent: Sure, let me know your travel dates please?",
"customer: The trip dates are from 01 Feb to 03 Feb.",
"agent: Can I know your airport codes?",
"customer: The airport codes are from PHL to OAK.",
"agent: Any preferences you have?",
"customer: No, that is it.",
"agent: Spirit Airlines has a connecting flight with number 1018 for fare 200, shall I proceed?",
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"customer: Hello.",
"agent: Hello. How can I help you?",
"customer: I am Gregory Allen. I want to attend a live music concert in EWr, can you please find a flight from ORD?",
"agent: Sure, I will help you to book a flight. Can you please share me your travelling dates?",
"customer: I want to start my journey on 10/10 and my return will be on 10/12.",
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"agent: Here, I found a flight for you with single connection. Shall I proceed for booking?",
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"agent: Your ticket is booked with flight number 1000 and airline is Southwest.",
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{
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"customer: Hello, I am Richard Perez.",
"agent: Hello, how may I be your helping hand?",
"customer: I have an meeting at NV - LAS, with my company CEO. As per my company rules I need to travel only in a connecting flight.",
"agent: Sure, do you have any fare limit?",
"customer: My fare is 1000.",
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"customer: My travelling dates are from 05/15 to 05/17.",
"agent: Can you let me know your source code?",
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"customer: Hello.",
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"customer: Can you please help me in booking a flight ticket from Denver, DEN to Phoenix, PHX?",
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{
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"customer: Hello.",
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"customer: I am starting on 06/18 and return back on 06/20.",
"agent: I found a flight 1018 of AA airlines matches with your preferences, can I proceed?",
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"agent: Thank you, have a good day."
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{
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"customer: Hi.",
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"customer: I want to cancel my reservation which is done recently due to some problems, can you please help me?",
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{
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"customer: Hi.",
"agent: Hello. How may I help you out?",
"customer: I need to book a flight ticket from OAK to ORD, can you help me in booking a flight ticket?",
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"agent: Can I know your name please?",
"customer: Sure, Rebecca Moore here.",
"agent: Do you need any specifications?",
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"agent: There is a flight with connection 1 and price 100, can I proceed?",
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{
"status": "book",
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"customer: Hello.",
"agent: Hello, How can I aid you?",
"customer: I am Amy Mitchell want to travel to MA- BOS to enjoy beach holidays with my cousins.",
"agent: Ok.",
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"agent: Thanks for waiting. You have a single connecting flight in JetBlue airline and flight number 1017 having price 100. Shall I proceed?",
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"agent: Thank you."
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"status": "no_flight",
"name": "Daniel Moore",
"flight": []
} | {
"return_month": "June",
"return_day": "16",
"max_price": 1000,
"departure_airport": "JFK",
"max_connections": 1,
"departure_day": "14",
"goal": "book",
"departure_month": "June",
"name": "Daniel Moore",
"return_airport": "CLT"
} | [
1519346135,
1519346139,
1519346174,
1519346200,
1519346211,
1519346214,
1519346242,
1519346262,
1519346293,
1519346319,
1519346361,
1519346372,
1519346383,
1519346409,
1519346442,
1519346446
] | [
"customer: Hi. I am Daniel Moore.",
"agent: Hello, how can I help you?",
"customer: I want to attend a political meeting at Charlotte. Can you help me in my reservation?",
"agent: Sure, I will help you.",
"customer: Thank you.",
"agent: Please provide me your travelling dates?",
"customer: I Will start my journey on 06/14. I will return back on 06/16.",
"agent: Kindly mention me your airport codes?",
"customer: My source airport code is New York, NY - JFK. My destination airport code is Charlotte, NC - CLT.",
"agent: Do you have any specifications?",
"customer: I need a layover in my journey. I can pay up to 1000.",
"agent: Please wait a minute.",
"customer: Sure.",
"agent: Thanks for waiting, sorry to inform you that there are no flights with your specifications.",
"customer: Not an issue. Thank you for your kind information.",
"agent: Thank you for reaching us."
] | {
"status": "book",
"name": "Daniel Moore",
"flight": [
1021
]
} | [] | false |
{
"status": "book",
"name": "Rachel Wilson",
"flight": [
1006
]
} | {
"return_month": "Feb",
"return_day": "20",
"max_price": 5000,
"departure_airport": "PHL",
"max_connections": 1,
"departure_day": "18",
"goal": "book",
"departure_month": "Feb",
"name": "Rachel Wilson",
"return_airport": "DEN"
} | [
1513817871,
1513817881,
1513817946,
1513818008,
1513818057,
1513818102,
1513818120,
1513818212,
1513818245,
1513818266,
1513818268
] | [
"customer: Hello, I am Rachel Wilson.",
"agent: Hello, how can I aid you today?",
"customer: Can you help me with a connection flight to Denver, CO-DEN from PA-PHL?",
"agent: Sure, could you provide me your travel dates?",
"customer: I will start my journey from 02/18 and will be returning on 02/20.",
"agent: Ok, please wait a moment I will check and get back to you.",
"customer: Sure.",
"agent: We found a flight with your requirement, price is 100 with a connection in between the journey. Your ticket is confirmed with Delta airline and flight number is 1006.",
"customer: Thanks for the information.",
"agent: Thanks for reaching us, have a good day.",
"customer: Thank you."
] | {
"status": "book",
"name": "Rachel Wilson",
"flight": [
1006
]
} | [] | true |
End of preview. Expand
in Dataset Viewer.
Dataset Card for air_dialogue
Dataset Summary
AirDialogue, is a large dataset that contains 402,038 goal-oriented conversations. To collect this dataset, we create a contextgenerator which provides travel and flight restrictions. Then the human annotators are asked to play the role of a customer or an agent and interact with the goal of successfully booking a trip given the restrictions.
News in v1.3:
- We have included the test split of the AirDialogue dataset.
- We have included the meta context for OOD2 in the original AirDialogue paper.
Supported Tasks and Leaderboards
We use perplexity and BLEU score to evaluate the quality of the language generated by the model. We also compare the dialogue state generated by the model s and the ground truth state s0. Two categories of the metrics are used: exact match scores and scaled scores
The inference competition & leaderboard can be found here: https://worksheets.codalab.org/worksheets/0xa79833f4b3c24f4188cee7131b120a59
Languages
The text in the dataset is in English. The BCP 47 code is en
Dataset Structure
Data Instances
The data is provided in two set of files. The first one has the dialogues (air_dialogue_data) and the knowledge-base (air_dialogue_kb)
BuilderConfig: air_dialogue_data
{"action": {"status": "book", "name": "Emily Edwards", "flight": [1027]}, "intent": {"return_month": "June", "return_day": "14", "max_price": 200, "departure_airport": "DFW", "return_time": "afternoon", "max_connections": 1, "departure_day": "12", "goal": "book", "departure_month": "June", "name": "Emily Edwards", "return_airport": "IAD"}, "timestamps": [1519233239, 1519233244, 1519233249, 1519233252, 1519233333, 1519233374, 1519233392, 1519233416, 1519233443, 1519233448, 1519233464, 1519233513, 1519233525, 1519233540, 1519233626, 1519233628, 1519233638], "dialogue": ["customer: Hello.", "agent: Hello.", "customer: My name is Emily Edwards.", "agent: How may I help you out?", "customer: I need some help in my flight ticket reservation to attend a convocation meeting, can you please help me?", "agent: Sure, I will help you out. May I know your travelling dates please?", "customer: Thank you and my dates are 06/12 and back on 06/14.", "agent: Can I know your airport codes?", "customer: The airport codes are from DFW to IAD.", "agent: Ok, please wait a moment.", "customer: Sure.", "agent: There is a flight with connection 1 and price 200, can I proceed with this flight?", "customer: Yes, do proceed with booking.", "agent: Ok, your ticket has been booked.", "customer: Thank you for your assistance in my flight ticket reservation.", "agent: Thank you for choosing us.", "customer: You are welcome."], "expected_action": {"status": "book", "name": "Emily Edwards", "flight": [1027]}, "correct_sample": true}
BuilderConfig: air_dialogue_kb
{"kb": [{"return_airport": "DTW", "airline": "Spirit", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1000, "departure_month": "June", "departure_time_num": 17, "class": "economy", "return_time_num": 2, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "DTW", "airline": "Frontier", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1001, "departure_month": "June", "departure_time_num": 0, "class": "business", "return_time_num": 15, "return_month": "June", "return_day": "13", "num_connections": 0, "price": 500}, {"return_airport": "DTW", "airline": "JetBlue", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1002, "departure_month": "June", "departure_time_num": 0, "class": "business", "return_time_num": 13, "return_month": "June", "return_day": "13", "num_connections": 1, "price": 600}, {"return_airport": "IAD", "airline": "Hawaiian", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1003, "departure_month": "June", "departure_time_num": 6, "class": "economy", "return_time_num": 5, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "DFW", "airline": "AA", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1004, "departure_month": "June", "departure_time_num": 9, "class": "economy", "return_time_num": 11, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "IAD", "airline": "AA", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1005, "departure_month": "June", "departure_time_num": 3, "class": "economy", "return_time_num": 17, "return_month": "June", "return_day": "13", "num_connections": 1, "price": 100}, {"return_airport": "DTW", "airline": "Frontier", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1006, "departure_month": "June", "departure_time_num": 10, "class": "economy", "return_time_num": 10, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "IAD", "airline": "UA", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1007, "departure_month": "June", "departure_time_num": 14, "class": "economy", "return_time_num": 20, "return_month": "June", "return_day": "13", "num_connections": 1, "price": 100}, {"return_airport": "DFW", "airline": "AA", "departure_day": "13", "departure_airport": "DTW", "flight_number": 1008, "departure_month": "June", "departure_time_num": 6, "class": "economy", "return_time_num": 8, "return_month": "June", "return_day": "14", "num_connections": 2, "price": 400}, {"return_airport": "DFW", "airline": "Delta", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1009, "departure_month": "June", "departure_time_num": 18, "class": "economy", "return_time_num": 6, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "DFW", "airline": "Frontier", "departure_day": "13", "departure_airport": "DTW", "flight_number": 1010, "departure_month": "June", "departure_time_num": 4, "class": "economy", "return_time_num": 2, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "DFW", "airline": "Southwest", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1011, "departure_month": "June", "departure_time_num": 17, "class": "economy", "return_time_num": 22, "return_month": "June", "return_day": "13", "num_connections": 0, "price": 100}, {"return_airport": "DTW", "airline": "JetBlue", "departure_day": "11", "departure_airport": "DFW", "flight_number": 1012, "departure_month": "June", "departure_time_num": 13, "class": "economy", "return_time_num": 22, "return_month": "June", "return_day": "13", "num_connections": 1, "price": 100}, {"return_airport": "DTW", "airline": "Southwest", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1013, "departure_month": "June", "departure_time_num": 16, "class": "economy", "return_time_num": 13, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "DTW", "airline": "Delta", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1014, "departure_month": "June", "departure_time_num": 0, "class": "economy", "return_time_num": 8, "return_month": "June", "return_day": "15", "num_connections": 1, "price": 100}, {"return_airport": "DTW", "airline": "Southwest", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1015, "departure_month": "June", "departure_time_num": 17, "class": "economy", "return_time_num": 1, "return_month": "June", "return_day": "15", "num_connections": 1, "price": 300}, {"return_airport": "DTW", "airline": "UA", "departure_day": "11", "departure_airport": "DFW", "flight_number": 1016, "departure_month": "June", "departure_time_num": 10, "class": "economy", "return_time_num": 4, "return_month": "June", "return_day": "14", "num_connections": 0, "price": 200}, {"return_airport": "DFW", "airline": "AA", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1017, "departure_month": "June", "departure_time_num": 14, "class": "economy", "return_time_num": 23, "return_month": "June", "return_day": "14", "num_connections": 2, "price": 400}, {"return_airport": "DTW", "airline": "JetBlue", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1018, "departure_month": "June", "departure_time_num": 3, "class": "economy", "return_time_num": 1, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "DFW", "airline": "Hawaiian", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1019, "departure_month": "June", "departure_time_num": 7, "class": "economy", "return_time_num": 18, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "DFW", "airline": "Delta", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1020, "departure_month": "June", "departure_time_num": 6, "class": "economy", "return_time_num": 18, "return_month": "June", "return_day": "14", "num_connections": 2, "price": 200}, {"return_airport": "IAD", "airline": "Delta", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1021, "departure_month": "June", "departure_time_num": 11, "class": "business", "return_time_num": 8, "return_month": "June", "return_day": "14", "num_connections": 0, "price": 1000}, {"return_airport": "IAD", "airline": "JetBlue", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1022, "departure_month": "June", "departure_time_num": 4, "class": "economy", "return_time_num": 14, "return_month": "June", "return_day": "13", "num_connections": 0, "price": 200}, {"return_airport": "IAD", "airline": "Frontier", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1023, "departure_month": "June", "departure_time_num": 19, "class": "economy", "return_time_num": 23, "return_month": "June", "return_day": "13", "num_connections": 1, "price": 200}, {"return_airport": "DFW", "airline": "UA", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1024, "departure_month": "June", "departure_time_num": 11, "class": "economy", "return_time_num": 19, "return_month": "June", "return_day": "15", "num_connections": 1, "price": 200}, {"return_airport": "DTW", "airline": "Hawaiian", "departure_day": "11", "departure_airport": "IAD", "flight_number": 1025, "departure_month": "June", "departure_time_num": 6, "class": "economy", "return_time_num": 10, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "DTW", "airline": "UA", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1026, "departure_month": "June", "departure_time_num": 0, "class": "economy", "return_time_num": 18, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 300}, {"return_airport": "IAD", "airline": "Delta", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1027, "departure_month": "June", "departure_time_num": 17, "class": "economy", "return_time_num": 15, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "IAD", "airline": "Southwest", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1028, "departure_month": "June", "departure_time_num": 23, "class": "economy", "return_time_num": 13, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "DFW", "airline": "Spirit", "departure_day": "11", "departure_airport": "DTW", "flight_number": 1029, "departure_month": "June", "departure_time_num": 22, "class": "business", "return_time_num": 4, "return_month": "June", "return_day": "14", "num_connections": 0, "price": 800}], "reservation": 0}
Data Fields
BuilderConfig: air_dialogue_data:
Provides for customer context, dialogue states and environment
| key name | Description |
|---|---|
| 'search_action' | search action performed by customer |
| 'action' | Action taken by the agent |
| 'intent' | Intents from the conversation |
| 'timestamps' | Timestamp for each of the dialogues |
| 'dialogue' | Dialogue recorded between agent & customer |
| 'expected_action' | Expected action from agent (human-annotated) |
| 'correct_sample' | whether action performed by agent was same as expected_action |
BuilderConfig: air_dialogue_kb:
Provides for the Agent Context ca = (db, r )
| key name | Description |
|---|---|
| 'kb' | Available flights in the database |
| 'reservation' | whether customer has an existing reservation |
Data Splits
Data is split into Train/Dev & Test in the ration of 80%, 10% and 10%
Dataset Creation
Curation Rationale
[Needs More Information]
Source Data
Initial Data Collection and Normalization
[Needs More Information]
Who are the source language producers?
[Needs More Information]
Annotations
Annotation process
To collect this dataset, we create a contextgenerator which provides travel and flight restrictions. We then ask human annotators to play the role of a customer or an agent and interact with the goal of successfully booking a trip given the restrictions. Key to our environment is the ease of evaluating the success of the dialogue, which is achieved by using ground-truth states (e.g., the flight being booked) generated by the restrictions. Any dialogue agent that does not generate the correct states is considered to fail.
Who are the annotators?
[Needs More Information]
Personal and Sensitive Information
No personal and sensitive information is stored
Considerations for Using the Data
Social Impact of Dataset
[Needs More Information]
Discussion of Biases
[Needs More Information]
Other Known Limitations
[Needs More Information]
Additional Information
Dataset Curators
For issues regarding HuggingFace Dataset Hub implementation Aakash Gupta
Licensing Information
cc-by-nc-4.0
Citation Information
@inproceedings{wei-etal-2018-airdialogue,
title = "{A}ir{D}ialogue: An Environment for Goal-Oriented Dialogue Research",
author = "Wei, Wei and
Le, Quoc and
Dai, Andrew and
Li, Jia",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1419",
doi = "10.18653/v1/D18-1419",
pages = "3844--3854",
abstract = "Recent progress in dialogue generation has inspired a number of studies on dialogue systems that are capable of accomplishing tasks through natural language interactions. A promising direction among these studies is the use of reinforcement learning techniques, such as self-play, for training dialogue agents. However, current datasets are limited in size, and the environment for training agents and evaluating progress is relatively unsophisticated. We present AirDialogue, a large dataset that contains 301,427 goal-oriented conversations. To collect this dataset, we create a context-generator which provides travel and flight restrictions. We then ask human annotators to play the role of a customer or an agent and interact with the goal of successfully booking a trip given the restrictions. Key to our environment is the ease of evaluating the success of the dialogue, which is achieved by using ground-truth states (e.g., the flight being booked) generated by the restrictions. Any dialogue agent that does not generate the correct states is considered to fail. Our experimental results indicate that state-of-the-art dialogue models can only achieve a score of 0.17 while humans can reach a score of 0.91, which suggests significant opportunities for future improvement.",
}
Contributions
Thanks to @skyprince999 for adding this dataset.
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