Datasets:
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Annotations Creators:
machine-generated
Source Datasets:
original
ArXiv:
Tags:
License:
dialogue_id
stringlengths 7
9
| services
sequence | turns
sequence |
|---|---|---|
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"I am feeling hungry so I would like to find a place to eat.",
"Do you have a specific which you want the eating place to be located at?",
"I would like for it to be in San Jose.",
"Is there a specific cuisine type you enjoy, such as Mexican, Italian or something else?",
"I usually like eating the American type of food.",
"I see that at 71 Saint Peter there is a good restaurant which is in San Jose.",
"Can you give me the address of this restaurant.",
"If you want to go to this restaurant you can find it at 71 North San Pedro Street.",
"Can you give me the phone number that I can contact them with?",
"If you want to phone them you can at 408-971-8523.",
"Is there some other restaurant which you can suggest?",
"How would you like Bazille restaurant which is situated in San Jose.",
"Do you have another restaurant matching my needs? For example a restaurant which is economical and is located in Palo Alto.",
"I see that 7 restaurants suit to what you requested. Bird Dog seems as a good restaurant and is located in Palo Alto.",
"Alright, that seems good. I would like to make a booking at this restaurant.",
"For which time do you want the booking to be?",
"I will be eating there at 11:30 am so make it for then.",
"Can you please confirm that you want to book a table for 2 at 11:30 am at the Bird Dog restaurant in Palo Alto for today.",
"That suits me well. Can you tell me if they feature live music?",
"Your booking has been made without errors, but unfortunately they do not have live music.",
"Will I be able to find liquor there? Can you give me the address of their location?",
"The restaurant is located at 420 Ramona Street. Unfortunately they do not serve alcohol at the restaurant.",
"I appreciate it very much. That would be all.",
"Have a good time!"
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"Sure, I will help you, What type of food are you looking for? Which city should i search in?",
"Some Punjabi kind of foods in milpitas",
"I found a good restaurant in Milpitas, The restaurant name is Anjappar Chettinad Restaurant",
"Ok, What is the address? How pricey are they?",
"Price range is moderate, The address is Milpitas Square",
"Ok, Find me another restaurant?",
"I found another restaurant for you, Aria Dining & Banquets Fine Indian Cuisine Restaurant at Milpitas",
"No, suggest me another Restaurant, Some Take-out type of food will be perfect",
"I found 2 Restaurants, Olive garden Italian Restaurant at Milpitas",
"Yes, it seems good for me",
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"Yes, I want it on the 11th",
"please tell me what time is the reservation for?",
"at afternoon 1:30",
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"No, time change to twelve in the afternoon for one people",
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"Yes",
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"What is the Restaurant address, Do they have live music, I love it",
"Sorry, They don't have live music, The restaurant address is 1350 Great Mall Drive",
"Thank you very much for your help",
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"No, Thanks",
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1_00005 | [
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"I want to eat in San Bruno",
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"What about Isla Restaurant in San Bruno?",
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"It is moderate pricing and they are at 448 San Mateo Avenue",
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"On the 12th at six pm",
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} |
1_00007 | [
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"Can I please have the address and contact number?",
"408-227-5502 is their phone and it is located at 436 Blossom Hill Road.",
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"There is another nice restaurant called Antonella's Ristorante in San Jose.",
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"Yes, thank you that is correct.",
"I have successful made your reservation.",
"Thank you so much for setting that up.",
"Is there anything else that I can help you with.",
"That is all thank you so much.",
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} |
1_00008 | [
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"Can you find me something economical to eat in Pleasanton.",
"Are you looking for Italian, Indian or something else?",
"I would like some Italian food.",
"Found 5 restaurants for you out of which De La Torre's Trattoria seems a great from Pleasanton.",
"Can you provide the address and do they have live music?",
"No, they do not have live music and its address is 6025 West Las Positas Boulevard.",
"Any other recommendation?",
"Forno Vecchio also seems nice and its located n Pleasanton as well.",
"Can you provide more options?",
"Another good restaurant in Pleasanton in Gay 90's Pizza Co.",
"That sounds like a good choice for me.",
"Is there anything else?",
"Yes, make a reservation at the restaurant.",
"What time do you want me to make the reservation?",
"I'll reach there at 1:30 pm.",
"Please confirm that you want to make a reservation at Gay 90's Pizza Co. which is located in Pleasanton for 2 people. The reservation is for today at 1:30 pm.",
"No, change the time to 7 pm and for one person only.",
"Alright, please confirm that the reservation is at 7 pm for 1 person.",
"No, change it on next friday.",
"Sure. Please confirm that the date is now next Friday and for 1 person.",
"Yes. Sounds good.",
"Your reservation is booked.",
"What is the address of the restaurant and do the have live music?",
"The address is 288 Main Street and No, they don't have live music either.",
"Alright, Thank you.",
"Have a great day."
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"I'm sorry. I need to make it on Monday next week at half past 12 in the afternoon.",
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"Yes, I'm sorry for the confusion. Can you tell me their phone number please?",
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""
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"values": [
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{
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{
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"city",
"cuisine",
"date",
"has_live_music",
"party_size",
"phone_number",
"price_range",
"restaurant_name",
"serves_alcohol",
"street_address",
"time"
],
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"Asian",
"2019-03-10",
"False",
"2",
"510-979-1368",
"moderate",
"Asian Pearl Seafood Restaurant",
"False",
"43635 Boscell Road",
"13:30"
]
}
]
}
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{
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"start": [],
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[
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[
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[
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[
"2"
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[
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[
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"half past 1 in the afternoon"
]
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""
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{
"slot": [],
"start": [],
"exclusive_end": []
}
],
"state": [
{
"active_intent": "NONE",
"requested_slots": [],
"slot_values": {
"slot_name": [
"city",
"cuisine",
"date",
"party_size",
"restaurant_name",
"time"
],
"slot_value_list": [
[
"Fremont"
],
[
"Asian"
],
[
"March 10th"
],
[
"2"
],
[
"Asian Pearl Seafood REstaurant",
"Asian pearl Seafood Restaurant"
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[
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17
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{
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{
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}
]
}
]
} |
End of preview. Expand
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Dataset Card for The Schema-Guided Dialogue Dataset
Dataset Summary
The Schema-Guided Dialogue dataset (SGD) was developed for the Dialogue State Tracking task of the Eights Dialogue Systems Technology Challenge (dstc8). The SGD dataset consists of over 18k annotated multi-domain, task-oriented conversations between a human and a virtual assistant. These conversations involve interactions with services and APIs spanning 17 domains, ranging from banks and events to media, calendar, travel, and weather. For most of these domains, the SGD dataset contains multiple different APIs, many of which have overlapping functionalities but different interfaces, which reflects common real-world scenarios.
Supported Tasks and Leaderboards
This dataset is designed to serve as an effective test-bed for intent prediction, slot filling, state tracking (i.e., estimating the user's goal) and language generation, among other tasks for large-scale virtual assistants:
- Generative dialogue modeling or
dialogue-modeling: the text of the dialogues can be used to train a sequence model on the utterances. Performance on this task is typically evaluated with delexicalized-BLEU, inform rate and request success. - Intent state tracking, a
multi-class-classificationtask: predict the belief state of the user side of the conversation, performance is measured by F1. - Action prediction, a
parsingtask: parse an utterance into the corresponding dialog acts for the system to use. F1 is typically reported.
Languages
The text in the dataset is in English (en).
Dataset Structure
Data Instances
dialoguesconfiguration (default): Each dialogue is represented as a sequence of turns, each containing a user or system utterance. The annotations for each turn are grouped into frames, where each frame corresponds to a single service. The annotations for user turns include the active intent, the dialogue state and slot spans for the different slots values mentioned in the turn. For system turns, we have the system actions representing the semantics of the system utterance. Each system action is represented using a dialogue act with optional parameters.schemaconfiguration: In addition to the dialogues, for each service used in the dataset, a normalized representation of the interface exposed is provided as the schema. The schema contains details like the name of the service, the list of tasks supported by the service (intents) and the attributes of the entities used by the service (slots). The schema also contains natural language descriptions of the service, intents and slots which can be used for developing models which can condition their predictions on the schema.
Data Fields
Each dialog instance has the following fields:
dialogue_id: A unique identifier for a dialogue.services: A list of services present in the dialogue.turns: A list of annotated system or user utterances. Each turn consists of the following fields:speaker: The speaker for the turn. EitherUSERorSYSTEM.utterance: A string containing the natural language utterance.frames: A list of frames, each frame containing annotations for a single service and consists of the following fields:service: The name of the service corresponding to the frame. The slots and intents used in the following fields are taken from the schema of this service.slots: A list of slot spans in the utterance, only provided for non-categorical slots. Each slot span contains the following fields:slot: The name of the slot.start: The index of the starting character in the utterance corresponding to the slot value.exclusive_end: The index of the character just after the last character corresponding to the slot value in the utterance.
actions: A list of actions corresponding to the system. Each action has the following fields:act: The type of action.slot: (optional) A slot argument for some of the actions.values: (optional) A list of values assigned to the slot. If the values list is non-empty, then the slot must be present.canonical_values: (optional) The values in their canonicalized form as used by the service. It is a list of strings of the same length as values.
service_call: (system turns only, optional) The request sent to the service. It consists of the following fields:method: The name of the intent or function of the service or API being executed.parameters: A pair of lists of the same lengths:parameter_slot_namecontains slot names andparameter_canonical_valuecontains the corresponding values in their canonicalized form.
service_results: (system turns only, optional) A list of entities containing the results obtained from the service. It is only available for turns in which a service call is made. Each entity is represented as a pair of lists of the same length:service_slot_namecontains slot names andservice_canonical_valuecontains the corresponding canonical values.state: (user turns only) The dialogue state corresponding to the service. It consists of the following fields:active_intent: The intent corresponding to the service of the frame which is currently being fulfilled by the system. It takes the value "NONE" if none of the intents are active.requested_slots: A list of slots requested by the user in the current turn.slot_values: A pair of lists of the same lengths:slot_namecontains slot names andslot_value_listcontains the corresponding lists of strings. For categorical slots, this list contains a single value assigned to the slot. For non-categorical slots, all the values in this list are spoken variations of each other and are equivalent (e.g, "6 pm", "six in the evening", "evening at 6" etc.).
The mapping from the action ID and the action name is the following:
0: AFFIRM 1: AFFIRM_INTENT 2: CONFIRM 3: GOODBYE 4: INFORM 5: INFORM_COUNT 6: INFORM_INTENT 7: NEGATE 8: NEGATE_INTENT 9: NOTIFY_FAILURE 10: NOTIFY_SUCCESS 11: OFFER 12: OFFER_INTENT 13: REQUEST 14: REQUEST_ALTS 15: REQ_MORE 16: SELECT 17: THANK_YOU
Data Splits
The dataset is split into a train, validation, and test split with the following sizes:
| train | validation | test | |
|---|---|---|---|
| Number of dialogues | 16142 | 2482 | 4201 |
| Number of turns | 48426 | 7446 | 12603 |
Dataset Creation
Curation Rationale
The data was collected by first using a dialogue simulator to generate dialogue outlines first and then paraphrasing them to obtain natural utterances. Using a dialogue simulator ensures the coverage of a large variety of dialogue flows by filtering out similar flows in the simulation phase to create a diverse dataset, and dialogues can be generated with their annotation, as opposed to a Wizard-of-Oz setup which is prone to manual annotation errors.
Source Data
Initial Data Collection and Normalization
The dialogue outlines are first generated by a simulator. The dialogue simulator interacts with the services to generate dialogue outlines. It consists of two agents playing the roles of the user and the system, interacting with each other using a finite set of actions specified through dialogue acts over a probabilistic automaton designed to capture varied dialogue trajectories. It is worth noting that the simulation automaton does not include any domain-specific constraints: all domain-specific constraints are encoded in the schema and scenario.
The dialogue paraphrasing framework then converts the outlines generated by the simulator into a natural conversation. Users may refer to the slot values in the dialogue acts in various different ways during the conversation, e.g., “los angeles” may be referred to as “LA” or “LAX”. To introduce these natural variations in the slot values, different slot values are replaced with a randomly selected variation while being kept consistent across user turns in a dialogue. The actions are then converted to pseudo-natural language utterances using a set of manually defined action-to-text templates, and the resulting utterances for the different actions in a turn are concatenated together.
Finally, the dialogue transformed by these steps is sent to the crowd workers to be reformulated into more natural language. One crowd worker is tasked with paraphrasing all utterances of a dialogue to ensure naturalness and coherence. The crowd workers are asked to exactly repeat the slot values in their paraphrases so that the span indices for the slots can be recovered via string matching.
Who are the source language producers?
The language structure is machine-generated, and the language realizations are produced by crowd workers. The dataset paper does not provide demographic information for the crowd workers.
Annotations
Annotation process
The annotations are automatically obtained during the initial sampling process and by string matching after reformulation.
Who are the annotators?
[N/A]
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
The dataset was created by a team of researchers working at Google Mountain View.
Licensing Information
The dataset is released under CC BY-SA 4.0 license.
Citation Information
For the DSCT8 task, please cite:
@article{corr/abs-2002-01359,
author = {Abhinav Rastogi and
Xiaoxue Zang and
Srinivas Sunkara and
Raghav Gupta and
Pranav Khaitan},
title = {Schema-Guided Dialogue State Tracking Task at {DSTC8}},
journal = {CoRR},
volume = {abs/2002.01359},
year = {2020},
url = {https://arxiv.org/abs/2002.01359},
archivePrefix = {arXiv},
eprint = {2002.01359}
}
For the initial release paper please cite:
@inproceedings{aaai/RastogiZSGK20,
author = {Abhinav Rastogi and
Xiaoxue Zang and
Srinivas Sunkara and
Raghav Gupta and
Pranav Khaitan},
title = {Towards Scalable Multi-Domain Conversational Agents: The Schema-Guided
Dialogue Dataset},
booktitle = {The Thirty-Fourth {AAAI} Conference on Artificial Intelligence, {AAAI}
2020, The Thirty-Second Innovative Applications of Artificial Intelligence
Conference, {IAAI} 2020, The Tenth {AAAI} Symposium on Educational
Advances in Artificial Intelligence, {EAAI} 2020, New York, NY, USA,
February 7-12, 2020},
pages = {8689--8696},
publisher = {{AAAI} Press},
year = {2020},
url = {https://aaai.org/ojs/index.php/AAAI/article/view/6394}
}
Contributions
Thanks to @yjernite for adding this dataset.
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Repository:
Github repository for The Schema-Guided Dialogue Dataset
Paper:
Towards Scalable Multi-Domain Conversational Agents: The Schema-Guided Dialogue Dataset
Point of Contact:
abhirast@google.com
Size of downloaded dataset files:
1.24 GB
Size of the auto-converted Parquet files:
29.6 MB
Number of rows:
22,889