task-specific-datasets
A collection of NLU datasets in constrained domains.
List of datasets:
- Banking: online banking queries annotated with their corresponding intents.
- Span Extraction: the data used for the SpanConvert paper.
- NLU++: a challenging evaluation environment for dialogue NLU models (multi-domain, multi-label intents and slots).
- EVI: a challenging multilingual dataset for knowledge-based enrolment, identification, and identification in spoken dialogue systems.
Banking
Dataset composed of online banking queries annotated with their corresponding intents.
| Dataset statistics | |
|---|---|
| Train examples | 10003 |
| Test examples | 3080 |
| Number of intents | 77 |
| Example Query | Intent |
|---|---|
| Is there a way to know when my card will arrive? | card_arrival |
| I think my card is broken | card_not_working |
| I made a mistake and need to cancel a transaction | cancel_transfer |
| Is my card usable anywhere? | card_acceptance |
Citations
When using the banking dataset in your work, please cite Efficient Intent Detection with Dual Sentence Encoders.
@inproceedings{Casanueva2020,
author = {I{\~{n}}igo Casanueva and Tadas Temcinas and Daniela Gerz and Matthew Henderson and Ivan Vulic},
title = {Efficient Intent Detection with Dual Sentence Encoders},
year = {2020},
month = {mar},
note = {Data available at https://github.com/PolyAI-LDN/task-specific-datasets},
url = {https://arxiv.org/abs/2003.04807},
booktitle = {Proceedings of the 2nd Workshop on NLP for ConvAI - ACL 2020}
}
Span Extraction
The directory span_extraction contains the data used for the SpanConvert paper.
A training example looks like:
{
"userInput": {
"text": "I would like a table for one person"
},
"labels": [
{
"slot": "people",
"valueSpan": {
"startIndex": 25,
"endIndex": 35
}
}
]
}
In the above example, the span "one person" is the value for the people slot.
The datasets have a structure like this:
ls span_extraction/restaurant8k
test.json
train_0.json
train_1.json
train_2.json
...
Where:
test.jsoncontains the examples for evaluationtrain_0.jsoncontains all of the training examplestrain_{i}.jsoncontains1/(2^i)th of the training data.
Exploring the Span Extraction Datasets
Here's a quick command line demo to explore some of the datasets (requires jq and parallel)
# Calculate the number of examples in each json file.
cd span_extraction
ls -d restaurant8k/*.json | parallel -k 'echo -n "{}," && cat {} | jq length'
restaurant8k/test.json,3731
restaurant8k/train_0.json,8198
restaurant8k/train_1.json,4099
restaurant8k/train_2.json,2049
restaurant8k/train_3.json,1024
restaurant8k/train_4.json,512
restaurant8k/train_5.json,256
restaurant8k/train_6.json,128
restaurant8k/train_7.json,64
restaurant8k/train_8.json,32
ls -d dstc8/*/*.json | parallel -k 'echo -n "{}," && cat {} | jq length'
dstc8/Buses_1/test.json,377
dstc8/Buses_1/train_0.json,1133
dstc8/Buses_1/train_1.json,566
dstc8/Buses_1/train_2.json,283
dstc8/Buses_1/train_3.json,141
dstc8/Buses_1/train_4.json,70
dstc8/Events_1/test.json,521
dstc8/Events_1/train_0.json,1498
dstc8/Events_1/train_1.json,749
dstc8/Events_1/train_2.json,374
dstc8/Events_1/train_3.json,187
dstc8/Events_1/train_4.json,93
dstc8/Homes_1/test.json,587
dstc8/Homes_1/train_0.json,2064
dstc8/Homes_1/train_1.json,1032
dstc8/Homes_1/train_2.json,516
dstc8/Homes_1/train_3.json,258
dstc8/Homes_1/train_4.json,129
dstc8/RentalCars_1/test.json,328
dstc8/RentalCars_1/train_0.json,874
dstc8/RentalCars_1/train_1.json,437
dstc8/RentalCars_1/train_2.json,218
dstc8/RentalCars_1/train_3.json,109
dstc8/RentalCars_1/train_4.json,54
Citations
When using the datasets in your work, please cite the Span-ConveRT paper.
@inproceedings{CoopeFarghly2020,
Author = {Sam Coope and Tyler Farghly and Daniela Gerz and Ivan Vulić and Matthew Henderson},
Title = {Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational Representations},
Year = {2020},
url = {https://arxiv.org/abs/2005.08866},
publisher = {ACL},
}
License
The datasets shared on this repository are licensed under the license found in the LICENSE file.