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Error code: FeaturesError Exception: ValueError Message: Not able to read records in the JSON file at zip://data/attraction_db.json::hf://datasets/ConvLab/crosswoz@4a3e56082543ed9eecb9c76ef5eadc1aa0cc5ca0/data.zip. Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 240, in compute_first_rows_from_streaming_response iterable_dataset = iterable_dataset._resolve_features() File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2216, in _resolve_features features = _infer_features_from_batch(self.with_format(None)._head()) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1239, in _head return _examples_to_batch(list(self.take(n))) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1389, in __iter__ for key, example in ex_iterable: File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1044, in __iter__ yield from islice(self.ex_iterable, self.n) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 282, in __iter__ for key, pa_table in self.generate_tables_fn(**self.kwargs): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 165, in _generate_tables raise ValueError(f"Not able to read records in the JSON file at {file}.") from None ValueError: Not able to read records in the JSON file at zip://data/attraction_db.json::hf://datasets/ConvLab/crosswoz@4a3e56082543ed9eecb9c76ef5eadc1aa0cc5ca0/data.zip.
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Dataset Card for CrossWOZ
- Repository: https://github.com/thu-coai/CrossWOZ
- Paper: https://aclanthology.org/2020.tacl-1.19/
- 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('crosswoz')
ontology = load_ontology('crosswoz')
database = load_database('crosswoz')
For more usage please refer to here.
Dataset Summary
CrossWOZ is the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset. It contains 6K dialogue sessions and 102K utterances for 5 domains, including hotel, restaurant, attraction, metro, and taxi. Moreover, the corpus contains rich annotation of dialogue states and dialogue acts at both user and system sides. We also provide a user simulator and several benchmark models for pipelined taskoriented dialogue systems, which will facilitate researchers to compare and evaluate their models on this corpus.
- How to get the transformed data from original data:
- Run
python preprocess.py
in the current directory. Need../../crosswoz/
as the original data.
- Run
- Main changes of the transformation:
- Add simple description for domains, slots, and intents.
- Switch intent&domain of
General
dialog acts => domain == 'General' and intent in ['thank','bye','greet','welcome'] - Binary dialog acts include: 1) domain == 'General'; 2) intent in ['NoOffer', 'Request', 'Select']; 3) slot in ['酒店设施']
- Categorical dialog acts include: slot in ['酒店类型', '车型', '车牌']
- Non-categorical dialogue acts: others. assert intent in ['Inform', 'Recommend'] and slot != 'none' and value != 'none'
- Transform original user goal to list of
{domain: {'inform': {slot: [value, mentioned/not mentioned]}, 'request': {slot: [value, mentioned/not mentioned]}}}
, stored asuser_state
of user turns. - Transform
sys_state_init
(first API call of system turns) withoutselectedResults
as belief state in user turns. - Transform
sys_state
(last API call of system turns) todb_query
with domain states that contain non-emptyselectedResults
. TheselectedResults
are saved asdb_results
(only contain entity name). Both stored in system turns.
- Annotations:
- user goal, user state, dialogue acts, state, db query, db results.
- Multiple values in state are separated by spaces, meaning all constraints should be satisfied.
Supported Tasks and Leaderboards
NLU, DST, Policy, NLG, E2E, User simulator
Languages
Chinese
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 | 5012 | 84674 | 16.89 | 20.55 | 3.02 | 99.67 | - | 100 | 94.39 |
validation | 500 | 8458 | 16.92 | 20.53 | 3.04 | 99.62 | - | 100 | 94.36 |
test | 500 | 8476 | 16.95 | 20.51 | 3.08 | 99.61 | - | 100 | 94.85 |
all | 6012 | 101608 | 16.9 | 20.54 | 3.03 | 99.66 | - | 100 | 94.43 |
6 domains: ['景点', '餐馆', '酒店', '地铁', '出租', 'General']
- 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
@article{zhu2020crosswoz,
author = {Qi Zhu and Kaili Huang and Zheng Zhang and Xiaoyan Zhu and Minlie Huang},
title = {Cross{WOZ}: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset},
journal = {Transactions of the Association for Computational Linguistics},
year = {2020}
}
Licensing Information
Apache License, Version 2.0
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