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The dataset generation failed
Error code: DatasetGenerationError
Exception: DatasetGenerationError
Message: An error occurred while generating the dataset
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 583, in write_table
self._build_writer(inferred_schema=pa_table.schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 404, in _build_writer
self.pa_writer = self._WRITER_CLASS(self.stream, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 1016, in __init__
self.writer = _parquet.ParquetWriter(
File "pyarrow/_parquet.pyx", line 1869, in pyarrow._parquet.ParquetWriter.__cinit__
File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Cannot write struct type 'inform' with no child field to Parquet. Consider adding a dummy child field.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2027, in _prepare_split_single
num_examples, num_bytes = writer.finalize()
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 602, in finalize
self._build_writer(self.schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 404, in _build_writer
self.pa_writer = self._WRITER_CLASS(self.stream, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 1016, in __init__
self.writer = _parquet.ParquetWriter(
File "pyarrow/_parquet.pyx", line 1869, in pyarrow._parquet.ParquetWriter.__cinit__
File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Cannot write struct type 'inform' with no child field to Parquet. Consider adding a dummy child field.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1321, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 935, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2038, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Open a discussion for direct support.
dataset
string | original_id
string | goal
dict | domains
sequence | turns
list | dialogue_id
string | data_split
string |
|---|---|---|---|---|---|---|
sgd5 | 1_00000 | {
"description": "",
"inform": {},
"request": {}
} | [
"Restaurants_15"
] | [
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},
"utt_idx": 0,
"utterance": "I am feeling hungry so I would like to find a place to eat."
},
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},
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}
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},
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},
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},
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},
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},
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{
"domain": "Restaurants_15",
"end": 34,
"intent": "inform",
"slot": "type_of_food_available",
"start": 26,
"value": "American"
}
]
},
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},
"dialogue_acts": {
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{
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"intent": "req_more",
"slot": ""
}
],
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},
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},
"speaker": "system",
"state": null,
"utt_idx": 23,
"utterance": "Shall i help you with anything else?"
},
{
"active_intent": {
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"Restaurants_15": "NONE",
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},
"db_results": null,
"dialogue_acts": {
"binary": [
{
"domain": "",
"intent": "negate",
"slot": ""
},
{
"domain": "",
"intent": "thank_you",
"slot": ""
}
],
"categorical": [],
"non-categorical": []
},
"requested_slots": {
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"Banks_15": null,
"Banks_25": null,
"Buses_15": null,
"Buses_25": null,
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"Payment_15": null,
"RentalCars_15": null,
"RentalCars_25": null,
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"Restaurants_15": [],
"Restaurants_25": null,
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"Services_25": null,
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"Services_45": null,
"Trains_15": null,
"Travel_15": null,
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},
"service_call": null,
"speaker": "user",
"state": {
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"Payment_15": null,
"RentalCars_15": null,
"RentalCars_25": null,
"RentalCars_35": null,
"Restaurants_15": {
"address_of_restaurant": "",
"alcohol_served_yes_no": "",
"desired_reservation_date": "March 11th|the 11th",
"entertainment": "",
"estimated_price_band": "",
"location_of_the_restaurant": "Milpitas|milpitas",
"name_of_business": "Olive Garden Italian Restaurant|Olive garden Italian Restaurant",
"number_reservation_seats": "1",
"restaurant_contact": "",
"restaurant_reservation_time": "12 pm|twelve in the afternoon",
"type_of_food_available": "Take-out"
},
"Restaurants_25": null,
"RideSharing_15": null,
"RideSharing_25": null,
"Services_15": null,
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"Services_35": null,
"Services_45": null,
"Trains_15": null,
"Travel_15": null,
"Weather_15": null
},
"utt_idx": 24,
"utterance": "No, Thanks"
},
{
"active_intent": null,
"db_results": {
"Alarm_15": null,
"Banks_15": null,
"Banks_25": null,
"Buses_15": null,
"Buses_25": null,
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"Movies_15": null,
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"Music_25": null,
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"Payment_15": null,
"RentalCars_15": null,
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"Restaurants_15": null,
"Restaurants_25": null,
"RideSharing_15": null,
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"Services_25": null,
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"Services_45": null,
"Trains_15": null,
"Travel_15": null,
"Weather_15": null
},
"dialogue_acts": {
"binary": [
{
"domain": "",
"intent": "goodbye",
"slot": ""
}
],
"categorical": [],
"non-categorical": []
},
"requested_slots": null,
"service_call": {
"Alarm_15": null,
"Banks_15": null,
"Banks_25": null,
"Buses_15": null,
"Buses_25": null,
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"Events_25": null,
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"Flights_15": null,
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"Travel_15": null,
"Weather_15": null
},
"speaker": "system",
"state": null,
"utt_idx": 25,
"utterance": "You are welcome, have a great day"
}
] | sgd5-train-1 | train |
sgd5 | 1_00002 | {
"description": "",
"inform": {},
"request": {}
} | [
"Restaurants_15"
] | [{"active_intent":{"Alarm_15":null,"Banks_15":null,"Banks_25":null,"Buses_15":null,"Buses_25":null,"(...TRUNCATED) | sgd5-train-2 | train |
sgd5 | 1_00003 | {
"description": "",
"inform": {},
"request": {}
} | [
"Restaurants_15"
] | [{"active_intent":{"Alarm_15":null,"Banks_15":null,"Banks_25":null,"Buses_15":null,"Buses_25":null,"(...TRUNCATED) | sgd5-train-3 | train |
sgd5 | 1_00004 | {
"description": "",
"inform": {},
"request": {}
} | [
"Restaurants_15"
] | [{"active_intent":{"Alarm_15":null,"Banks_15":null,"Banks_25":null,"Buses_15":null,"Buses_25":null,"(...TRUNCATED) | sgd5-train-4 | train |
sgd5 | 1_00005 | {
"description": "",
"inform": {},
"request": {}
} | [
"Restaurants_15"
] | [{"active_intent":{"Alarm_15":null,"Banks_15":null,"Banks_25":null,"Buses_15":null,"Buses_25":null,"(...TRUNCATED) | sgd5-train-5 | train |
sgd5 | 1_00006 | {
"description": "",
"inform": {},
"request": {}
} | [
"Restaurants_15"
] | [{"active_intent":{"Alarm_15":null,"Banks_15":null,"Banks_25":null,"Buses_15":null,"Buses_25":null,"(...TRUNCATED) | sgd5-train-6 | train |
sgd5 | 1_00007 | {
"description": "",
"inform": {},
"request": {}
} | [
"Restaurants_15"
] | [{"active_intent":{"Alarm_15":null,"Banks_15":null,"Banks_25":null,"Buses_15":null,"Buses_25":null,"(...TRUNCATED) | sgd5-train-7 | train |
sgd5 | 1_00008 | {
"description": "",
"inform": {},
"request": {}
} | [
"Restaurants_15"
] | [{"active_intent":{"Alarm_15":null,"Banks_15":null,"Banks_25":null,"Buses_15":null,"Buses_25":null,"(...TRUNCATED) | sgd5-train-8 | train |
sgd5 | 1_00009 | {
"description": "",
"inform": {},
"request": {}
} | [
"Restaurants_15"
] | [{"active_intent":{"Alarm_15":null,"Banks_15":null,"Banks_25":null,"Buses_15":null,"Buses_25":null,"(...TRUNCATED) | sgd5-train-9 | train |
End of preview.
YAML Metadata
Warning:
The task_categories "conversational" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, other
Dataset Card for SGD-X v5
- Repository: https://github.com/google-research-datasets/dstc8-schema-guided-dialogue/tree/master/sgd_x
- Paper: https://arxiv.org/pdf/2110.06800.pdf
- Leaderboard: None
- Who transforms the dataset: Qi Zhu(zhuq96 at gmail dot com)
To use this dataset, you need to install ConvLab-3 platform first. Then you can load the dataset via:
from convlab.util import load_dataset, load_ontology, load_database
dataset = load_dataset('sgd5')
ontology = load_ontology('sgd5')
database = load_database('sgd5')
For more usage please refer to here.
Dataset Summary
The Schema-Guided Dialogue (SGD) dataset consists of over 20k annotated multi-domain, task-oriented conversations between a human and a virtual assistant. These conversations involve interactions with services and APIs spanning 20 domains, such as banks, events, media, calendar, travel, and weather. For most of these domains, the dataset contains multiple different APIs, many of which have overlapping functionalities but different interfaces, which reflects common real-world scenarios. The wide range of available annotations can be used for intent prediction, slot filling, dialogue state tracking, policy imitation learning, language generation, and user simulation learning, among other tasks for developing large-scale virtual assistants. Additionally, the dataset contains unseen domains and services in the evaluation set to quantify the performance in zero-shot or few-shot settings.
The SGD-X dataset consists of 5 linguistic variants of every schema in the original SGD dataset. Linguistic variants were written by hundreds of paid crowd-workers. In the SGD-X directory, v1 represents the variant closest to the original schemas and v5 the farthest in terms of linguistic distance. To evaluate model performance on SGD-X schemas, dialogues must be converted using the script generate_sgdx_dialogues.py.
- How to get the transformed data from original data:
- Download dstc8-schema-guided-dialogue-master.zip.
- Modified
sgd_x/generate_sgdx_dialogues.pyas https://github.com/google-research-datasets/dstc8-schema-guided-dialogue/issues/57 - Run
python -m sgd_x.generate_sgdx_dialoguesunderdstc8-schema-guided-dialogue-masterdir which need tensorflow installed. - Run
python preprocess.pyin the current directory.
- Main changes of the transformation:
- Lower case original
actasintent. - Add
countslot for each domain, non-categorical, find span by text matching. - Categorize
dialogue actsaccording to theintent. - Concatenate multiple values using
|. - Retain
active_intent,requested_slots,service_call.
- Lower case original
- Annotations:
- dialogue acts, state, db_results, service_call, active_intent, requested_slots.
Supported Tasks and Leaderboards
NLU, DST, Policy, NLG, E2E
Languages
English
Data Splits
| split | dialogues | utterances | avg_utt | avg_tokens | avg_domains | cat slot match(state) | cat slot match(goal) | cat slot match(dialogue act) | non-cat slot span(dialogue act) |
|---|---|---|---|---|---|---|---|---|---|
| train | 16142 | 329964 | 20.44 | 9.75 | 1.84 | 100 | - | 100 | 100 |
| validation | 2482 | 48726 | 19.63 | 9.66 | 1.84 | 100 | - | 100 | 100 |
| test | 4201 | 84594 | 20.14 | 10.4 | 2.02 | 100 | - | 100 | 100 |
| all | 22825 | 463284 | 20.3 | 9.86 | 1.87 | 100 | - | 100 | 100 |
45 domains: ['Banks_15', 'Buses_15', 'Buses_25', 'Calendar_15', 'Events_15', 'Events_25', 'Flights_15', 'Flights_25', 'Homes_15', 'Hotels_15', 'Hotels_25', 'Hotels_35', 'Media_15', 'Movies_15', 'Music_15', 'Music_25', 'RentalCars_15', 'RentalCars_25', 'Restaurants_15', 'RideSharing_15', 'RideSharing_25', 'Services_15', 'Services_25', 'Services_35', 'Travel_15', 'Weather_15', 'Alarm_15', 'Banks_25', 'Flights_35', 'Hotels_45', 'Media_25', 'Movies_25', 'Restaurants_25', 'Services_45', 'Buses_35', 'Events_35', 'Flights_45', 'Homes_25', 'Media_35', 'Messaging_15', 'Movies_35', 'Music_35', 'Payment_15', 'RentalCars_35', 'Trains_15']
- cat slot match: how many values of categorical slots are in the possible values of ontology in percentage.
- non-cat slot span: how many values of non-categorical slots have span annotation in percentage.
Citation
@inproceedings{lee2022sgd,
title={SGD-X: A Benchmark for Robust Generalization in Schema-Guided Dialogue Systems},
author={Lee, Harrison and Gupta, Raghav and Rastogi, Abhinav and Cao, Yuan and Zhang, Bin and Wu, Yonghui},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={36},
number={10},
pages={10938--10946},
year={2022}
}
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