Update files from the datasets library (from 1.3.0)
Browse filesRelease notes: https://github.com/huggingface/datasets/releases/tag/1.3.0
README.md
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- [Dataset Curators](#dataset-curators)
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- [Licensing Information](#licensing-information)
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- [Citation Information](#citation-information)
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## Dataset Description
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### Supported Tasks and Leaderboards
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[
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### Languages
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@@ -77,11 +80,11 @@ Sample data instance for `dialogue_domain` :
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"domain": "dmv",
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"turns": [
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{
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"da": "
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"role": "user",
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"utterance": "Hello, I forgot o update my address, can you help me with that?"
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},
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"role": "agent",
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"utterance": "hi, you have to report any change of address to DMV within 10 days after moving. You should do this both for the address associated with your license and all the addresses associated with all your vehicles."
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},
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],
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"role": "user",
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"utterance": "Can I do my DMV transactions online?"
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},
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{
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"da": "
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"role": "agent",
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"utterance": "Yes, you can sign up for MyDMV for all the online transactions needed."
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},
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{
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"da": "
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"role": "user",
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"utterance": "Thanks, and in case I forget to bring all of the documentation needed to the DMV office, what can I do?"
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},
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"da": "
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"role": "agent",
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"utterance": "This happens often with our customers so that's why our website and MyDMV are so useful for our customers. Just check if you can make your transaction online so you don't have to go to the DMV Office."
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},
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"role": "user",
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"utterance": "Ok, and can you tell me again where should I report my new address?"
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"role": "agent",
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"utterance": "Sure. Any change of address must be reported to the DMV, that's for the address associated with your license and any of your vehicles."
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},
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"role": "user",
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"utterance": "Can you tell me more about Traffic points and their cost?"
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},
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],
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"role": "agent",
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"is_impossible": false,
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"question": "Hello, I want to know about the retirement plan.",
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"answers": {
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"answer_end": [
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"answer_start": [
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],
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"\n\nBenefits Planner: Retirement \n\n\nOnline Calculator (WEP Version) \n"
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]
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},
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"dial_context": {
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"da": [
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"references": [
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"answer_start": [
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"text": [
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"utterance": [
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"title": "Benefits Planner: Retirement | Online Calculator (WEP Version)#1_0",
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"domain": "ssa"
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}
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- `turn_id`: the time order of the turn;
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- `role`: either "agent" or "user";
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- `da`: dialogue act;
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- `utterance`: the human-generated utterance based on the dialogue scene.
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For `doc2dial_rc`,
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- `id`: the ID of a QA instance;
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- `question`: user query;
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- `answers`: the answers that are grounded in the associated document;
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- `text`: the text content of the grounding span;
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- `title`: the title of the associated document;
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- `domain`: the domain of the associated document;
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publisher = "Association for Computational Linguistics",
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url = "https://www.aclweb.org/anthology/2020.emnlp-main.652",
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}
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- [Dataset Curators](#dataset-curators)
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- [Licensing Information](#licensing-information)
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- [Citation Information](#citation-information)
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- [Contributions](#contributions)
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## Dataset Description
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### Supported Tasks and Leaderboards
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> Supported Task: [Shared Task](https://doc2dial.github.io/workshop2021/shared.html) hosted by DialDoc21 at ACL.
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> Leaderboard: [LINK](https://eval.ai/web/challenges/challenge-page/793)
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### Languages
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"domain": "dmv",
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"turns": [
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"da": "query_condition",
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"references": [
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"sp_id": "4",
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"label": "precondition"
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],
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"role": "user",
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"utterance": "Hello, I forgot o update my address, can you help me with that?"
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"da": "response_solution",
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"references": [
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"sp_id": "6",
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"label": "solution"
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"sp_id": "7",
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"label": "solution"
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"sp_id": "4",
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"label": "references"
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"role": "agent",
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"utterance": "hi, you have to report any change of address to DMV within 10 days after moving. You should do this both for the address associated with your license and all the addresses associated with all your vehicles."
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"da": "query_solution",
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"references": [
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"sp_id": "56",
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"label": "solution"
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"sp_id": "48",
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"label": "references"
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],
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"role": "user",
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"utterance": "Can I do my DMV transactions online?"
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"da": "respond_solution",
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"references": [
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"sp_id": "56",
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"label": "solution"
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"sp_id": "48",
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"label": "references"
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],
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"role": "agent",
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"utterance": "Yes, you can sign up for MyDMV for all the online transactions needed."
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"da": "query_condition",
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"references": [
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"sp_id": "48",
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"label": "precondition"
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"role": "user",
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"utterance": "Thanks, and in case I forget to bring all of the documentation needed to the DMV office, what can I do?"
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"da": "respond_solution",
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"references": [
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"sp_id": "49",
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"label": "solution"
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"sp_id": "50",
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"label": "solution"
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"sp_id": "52",
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"label": "solution"
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"label": "references"
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"role": "agent",
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"utterance": "This happens often with our customers so that's why our website and MyDMV are so useful for our customers. Just check if you can make your transaction online so you don't have to go to the DMV Office."
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},
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"da": "query_solution",
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"sp_id": "6",
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"role": "user",
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"utterance": "Ok, and can you tell me again where should I report my new address?"
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"da": "respond_solution",
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"role": "agent",
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"utterance": "Sure. Any change of address must be reported to the DMV, that's for the address associated with your license and any of your vehicles."
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"label": "precondition"
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"role": "user",
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"utterance": "Can you tell me more about Traffic points and their cost?"
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"da": "respond_solution",
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"references": [
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"sp_id": "41",
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"label": "solution"
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"sp_id": "43",
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"label": "solution"
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"role": "agent",
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"is_impossible": false,
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"question": "Hello, I want to know about the retirement plan.",
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"answers": {
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"answer_start": [
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"\n\nBenefits Planner: Retirement \n\n\nOnline Calculator (WEP Version) \n"
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"context": "\n\nBenefits Planner: Retirement \n\n\nOnline Calculator (WEP Version) \nThe calculator shown below allows you to estimate your Social Security benefit. However , for the most accurate estimates , use the Detailed Calculator. You need to enter all your past earnings, which are shown on your online. Please Note: The Online Calculator is updated periodically * with new benefit increases and other benefit amounts. Therefore , it is likely that your benefit estimates in the future will differ from those calculated today. The Online Calculator works on PCs and Macs with Javascript enabled. Some browsers may not allow you to print the table below. Note: If your birthday is on January 1st , we figure your benefit as if your birthday was in the previous year. If you qualify for benefits as a Survivor , your full retirement age for survivors benefits may be different. ",
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|
330 |
"title": "Benefits Planner: Retirement | Online Calculator (WEP Version)#1_0",
|
331 |
"domain": "ssa"
|
332 |
}
|
|
|
369 |
- `turn_id`: the time order of the turn;
|
370 |
- `role`: either "agent" or "user";
|
371 |
- `da`: dialogue act;
|
372 |
+
- `references`: the grounding span (`id_sp`) in the associated document. If a turn is an irrelevant turn, i.e., `da` ends with "ood", `reference` is empty. **Note** that spans with labels "*precondition*"/"*solution*" are the actual grounding spans. Spans with label "*reference*" are the related titles or contextual reference, which is used for the purpose of describing a dialogue scene better to crowd contributors.
|
373 |
- `utterance`: the human-generated utterance based on the dialogue scene.
|
374 |
|
375 |
|
376 |
|
377 |
+
For `doc2dial_rc`, this conforms to [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) data format. For how to load Doc2Dial data for reading comprehension task, please refer [here](https://github.com/doc2dial/sharedtask-dialdoc2021).
|
378 |
|
379 |
- `id`: the ID of a QA instance;
|
380 |
- `question`: user query;
|
|
|
381 |
- `answers`: the answers that are grounded in the associated document;
|
382 |
+
- `answer_start`: the start position of the grounding span in the associated document (`context`);
|
|
|
383 |
- `text`: the text content of the grounding span;
|
|
|
|
|
|
|
|
|
|
|
|
|
384 |
- `title`: the title of the associated document;
|
385 |
- `domain`: the domain of the associated document;
|
386 |
+
- `context`: the text content of the associated document (without HTML markups).
|
|
|
387 |
|
388 |
|
389 |
|
|
|
457 |
publisher = "Association for Computational Linguistics",
|
458 |
url = "https://www.aclweb.org/anthology/2020.emnlp-main.652",
|
459 |
}
|
460 |
+
|
461 |
+
### Contributions
|
462 |
+
|
463 |
+
Thanks to [@songfeng](https://github.com/songfeng), [@KMFODA](https://github.com/KMFODA) for adding this dataset.
|
doc2dial.py
CHANGED
@@ -14,7 +14,7 @@
|
|
14 |
# limitations under the License.
|
15 |
|
16 |
# Lint as: python3
|
17 |
-
"""Doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset
|
18 |
|
19 |
from __future__ import absolute_import, division, print_function
|
20 |
|
@@ -44,22 +44,17 @@ in over 450 documents from four domains. Compared to the prior document-grounded
|
|
44 |
this dataset covers a variety of dialogue scenes in information-seeking conversations.
|
45 |
"""
|
46 |
|
47 |
-
_HOMEPAGE = "https://doc2dial.github.io
|
48 |
|
49 |
-
# TODO: Add the licence for the dataset here if you can find it
|
50 |
-
_LICENSE = ""
|
51 |
|
52 |
-
_URLs = "https://doc2dial.github.io/file/
|
53 |
|
54 |
|
55 |
class Doc2dial(datasets.GeneratorBasedBuilder):
|
56 |
-
"Doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset
|
57 |
|
58 |
-
VERSION = datasets.Version("1.1
|
59 |
|
60 |
-
# You will be able to load one or the other configurations in the following list with
|
61 |
-
# data = datasets.load_dataset("my_dataset", "first_domain")
|
62 |
-
# data = datasets.load_dataset("my_dataset", "second_domain")
|
63 |
BUILDER_CONFIGS = [
|
64 |
datasets.BuilderConfig(
|
65 |
name="dialogue_domain",
|
@@ -93,10 +88,10 @@ class Doc2dial(datasets.GeneratorBasedBuilder):
|
|
93 |
"turn_id": datasets.Value("int32"),
|
94 |
"role": datasets.Value("string"),
|
95 |
"da": datasets.Value("string"),
|
96 |
-
"
|
97 |
{
|
98 |
-
"
|
99 |
-
"
|
100 |
}
|
101 |
],
|
102 |
"utterance": datasets.Value("string"),
|
@@ -134,37 +129,16 @@ class Doc2dial(datasets.GeneratorBasedBuilder):
|
|
134 |
features = datasets.Features(
|
135 |
{
|
136 |
"id": datasets.Value("string"),
|
|
|
|
|
137 |
"question": datasets.Value("string"),
|
138 |
"answers": datasets.features.Sequence(
|
139 |
{
|
140 |
"text": datasets.Value("string"),
|
141 |
"answer_start": datasets.Value("int32"),
|
142 |
-
"answer_end": datasets.Value("int32"),
|
143 |
-
"sp_id": datasets.features.Sequence(datasets.Value("string")),
|
144 |
-
}
|
145 |
-
),
|
146 |
-
"is_impossible": datasets.Value("bool"),
|
147 |
-
"dial_context": datasets.features.Sequence(
|
148 |
-
{
|
149 |
-
"turn_id": datasets.Value("int32"),
|
150 |
-
"role": datasets.Value("string"),
|
151 |
-
"da": datasets.Value("string"),
|
152 |
-
"utterance": datasets.Value("string"),
|
153 |
-
"references": datasets.features.Sequence(
|
154 |
-
{
|
155 |
-
"text": datasets.Value("string"),
|
156 |
-
"answer_start": datasets.Value("int32"),
|
157 |
-
"answer_end": datasets.Value("int32"),
|
158 |
-
"sp_id": datasets.features.Sequence(datasets.Value("string")),
|
159 |
-
}
|
160 |
-
),
|
161 |
}
|
162 |
),
|
163 |
-
"doc_context": datasets.Value("string"),
|
164 |
-
"title": datasets.Value("string"),
|
165 |
"domain": datasets.Value("string"),
|
166 |
-
"start_candidates": datasets.features.Sequence(datasets.Value("int32")),
|
167 |
-
"end_candidates": datasets.features.Sequence(datasets.Value("int32")),
|
168 |
}
|
169 |
)
|
170 |
|
@@ -179,6 +153,7 @@ class Doc2dial(datasets.GeneratorBasedBuilder):
|
|
179 |
def _split_generators(self, dl_manager):
|
180 |
|
181 |
my_urls = _URLs
|
|
|
182 |
data_dir = dl_manager.download_and_extract(my_urls)
|
183 |
|
184 |
if self.config.name == "dialogue_domain":
|
@@ -186,13 +161,13 @@ class Doc2dial(datasets.GeneratorBasedBuilder):
|
|
186 |
datasets.SplitGenerator(
|
187 |
name=datasets.Split.TRAIN,
|
188 |
gen_kwargs={
|
189 |
-
"filepath": os.path.join(data_dir, "doc2dial/
|
190 |
},
|
191 |
),
|
192 |
datasets.SplitGenerator(
|
193 |
name=datasets.Split.VALIDATION,
|
194 |
gen_kwargs={
|
195 |
-
"filepath": os.path.join(data_dir, "doc2dial/
|
196 |
},
|
197 |
),
|
198 |
]
|
@@ -201,7 +176,7 @@ class Doc2dial(datasets.GeneratorBasedBuilder):
|
|
201 |
datasets.SplitGenerator(
|
202 |
name=datasets.Split.TRAIN,
|
203 |
gen_kwargs={
|
204 |
-
"filepath": os.path.join(data_dir, "doc2dial/
|
205 |
},
|
206 |
)
|
207 |
]
|
@@ -210,69 +185,45 @@ class Doc2dial(datasets.GeneratorBasedBuilder):
|
|
210 |
datasets.SplitGenerator(
|
211 |
name=datasets.Split.VALIDATION,
|
212 |
gen_kwargs={
|
213 |
-
"filepath": os.path.join(
|
214 |
-
data_dir, "doc2dial", "v0.9", "data", "woOOD", "doc2dial_dial_dev.json"
|
215 |
-
),
|
216 |
},
|
217 |
),
|
218 |
datasets.SplitGenerator(
|
219 |
name=datasets.Split.TRAIN,
|
220 |
gen_kwargs={
|
221 |
-
"filepath": os.path.join(
|
222 |
-
data_dir,
|
223 |
-
"doc2dial",
|
224 |
-
"v0.9",
|
225 |
-
"data",
|
226 |
-
"woOOD",
|
227 |
-
"doc2dial_dial_train.json",
|
228 |
-
),
|
229 |
},
|
230 |
),
|
231 |
]
|
232 |
|
233 |
def _load_doc_data_rc(self, filepath):
|
234 |
-
doc_filepath = os.path.join(os.path.dirname(filepath), "
|
235 |
with open(doc_filepath, encoding="utf-8") as f:
|
236 |
data = json.load(f)["doc_data"]
|
237 |
return data
|
238 |
|
239 |
-
def
|
240 |
-
"""
|
241 |
-
|
242 |
-
|
243 |
-
|
244 |
-
|
245 |
-
|
246 |
-
|
247 |
-
|
248 |
-
|
249 |
-
|
250 |
-
|
251 |
-
|
252 |
-
|
253 |
-
|
254 |
-
|
255 |
-
|
256 |
-
|
257 |
-
|
258 |
-
all_consecutive_spans.append(consecutive_spans)
|
259 |
-
consecutive_spans = [id_]
|
260 |
-
all_consecutive_spans.append(consecutive_spans)
|
261 |
-
if len(all_consecutive_spans) > 1:
|
262 |
-
all_consecutive_spans.reverse()
|
263 |
-
for con_spans in all_consecutive_spans:
|
264 |
-
answer = {
|
265 |
-
"answer_start": spans[con_spans[0]]["start_sp"],
|
266 |
-
"answer_end": spans[con_spans[-1]]["end_sp"],
|
267 |
-
"text": doc_text[spans[con_spans[0]]["start_sp"] : spans[con_spans[-1]]["end_sp"]],
|
268 |
-
"sp_id": con_spans,
|
269 |
-
}
|
270 |
-
output.append(answer)
|
271 |
-
return output
|
272 |
|
273 |
def _generate_examples(self, filepath):
|
274 |
"""This function returns the examples in the raw (text) form."""
|
275 |
-
|
276 |
if self.config.name == "dialogue_domain":
|
277 |
logging.info("generating examples from = %s", filepath)
|
278 |
with open(filepath, encoding="utf-8") as f:
|
@@ -285,22 +236,7 @@ class Doc2dial(datasets.GeneratorBasedBuilder):
|
|
285 |
"dial_id": dialogue["dial_id"],
|
286 |
"domain": domain,
|
287 |
"doc_id": doc_id,
|
288 |
-
"turns": [
|
289 |
-
{
|
290 |
-
"turn_id": i["turn_id"],
|
291 |
-
"role": i["role"],
|
292 |
-
"da": i["da"],
|
293 |
-
"reference": [
|
294 |
-
{
|
295 |
-
"keys": ref,
|
296 |
-
"values": str(i["reference"][ref]),
|
297 |
-
}
|
298 |
-
for ref in i["reference"]
|
299 |
-
],
|
300 |
-
"utterance": i["utterance"],
|
301 |
-
}
|
302 |
-
for i in dialogue["turns"]
|
303 |
-
],
|
304 |
}
|
305 |
|
306 |
yield dialogue["dial_id"], x
|
@@ -340,33 +276,22 @@ class Doc2dial(datasets.GeneratorBasedBuilder):
|
|
340 |
"doc_html_ts": data["doc_data"][domain][doc_id]["doc_html_ts"],
|
341 |
"doc_html_raw": data["doc_data"][domain][doc_id]["doc_html_raw"],
|
342 |
}
|
|
|
343 |
elif self.config.name == "doc2dial_rc":
|
344 |
"""Load dialog data in the reading comprehension task setup, where context is the grounding document,
|
345 |
input query is dialog history in reversed order, and output to predict is the next agent turn."""
|
346 |
|
347 |
logging.info("generating examples from = %s", filepath)
|
348 |
-
|
349 |
doc_data = self._load_doc_data_rc(filepath)
|
350 |
with open(filepath, encoding="utf-8") as f:
|
351 |
dial_data = json.load(f)["dial_data"]
|
352 |
for domain, d_doc_dials in dial_data.items():
|
353 |
for doc_id, dials in d_doc_dials.items():
|
354 |
doc = doc_data[domain][doc_id]
|
355 |
-
(
|
356 |
-
start_pos_char_candidates,
|
357 |
-
end_pos_char_candidates,
|
358 |
-
) = self._get_start_end_candidates_rc(doc["spans"])
|
359 |
for dial in dials:
|
360 |
all_prev_utterances = []
|
361 |
-
all_prev_turns = []
|
362 |
for idx, turn in enumerate(dial["turns"]):
|
363 |
-
all_prev_utterances.append(turn["utterance"])
|
364 |
-
if "references" not in turn:
|
365 |
-
turn["references"] = self._create_answers_merging_text_ref_rc(
|
366 |
-
turn["reference"], doc["spans"], doc["doc_text"]
|
367 |
-
)
|
368 |
-
turn.pop("reference", None)
|
369 |
-
all_prev_turns.append(turn)
|
370 |
if turn["role"] == "agent":
|
371 |
continue
|
372 |
if idx + 1 < len(dial["turns"]):
|
@@ -374,29 +299,18 @@ class Doc2dial(datasets.GeneratorBasedBuilder):
|
|
374 |
turn_to_predict = dial["turns"][idx + 1]
|
375 |
else:
|
376 |
continue
|
377 |
-
question = " ".join(list(reversed(all_prev_utterances)))
|
378 |
-
id_ = dial["dial_id"]
|
379 |
qa = {
|
380 |
"id": id_,
|
381 |
-
"question": question,
|
382 |
-
"answers": [],
|
383 |
-
"dial_context": all_prev_turns,
|
384 |
-
"doc_context": doc["doc_text"],
|
385 |
"title": doc_id,
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
386 |
"domain": domain,
|
387 |
-
"start_candidates": start_pos_char_candidates,
|
388 |
-
"end_candidates": end_pos_char_candidates,
|
389 |
}
|
390 |
-
if "references" not in turn_to_predict:
|
391 |
-
turn_to_predict["references"] = self._create_answers_merging_text_ref_rc(
|
392 |
-
turn_to_predict["reference"], doc["spans"], doc["doc_text"]
|
393 |
-
)
|
394 |
-
if not turn_to_predict["references"]:
|
395 |
-
qa["is_impossible"] = True
|
396 |
-
else:
|
397 |
-
qa["is_impossible"] = False
|
398 |
-
qa["answers"] = turn_to_predict["references"]
|
399 |
-
assert (
|
400 |
-
len((qa["answers"])) >= 1
|
401 |
-
), "Ensure the answers are not empty if the question is answerable"
|
402 |
yield id_, qa
|
|
|
14 |
# limitations under the License.
|
15 |
|
16 |
# Lint as: python3
|
17 |
+
"""Doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset v1.0.1"""
|
18 |
|
19 |
from __future__ import absolute_import, division, print_function
|
20 |
|
|
|
44 |
this dataset covers a variety of dialogue scenes in information-seeking conversations.
|
45 |
"""
|
46 |
|
47 |
+
_HOMEPAGE = "https://doc2dial.github.io"
|
48 |
|
|
|
|
|
49 |
|
50 |
+
_URLs = "https://doc2dial.github.io/file/doc2dial_v1.0.1.zip"
|
51 |
|
52 |
|
53 |
class Doc2dial(datasets.GeneratorBasedBuilder):
|
54 |
+
"Doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset v1.0.1"
|
55 |
|
56 |
+
VERSION = datasets.Version("1.0.1")
|
57 |
|
|
|
|
|
|
|
58 |
BUILDER_CONFIGS = [
|
59 |
datasets.BuilderConfig(
|
60 |
name="dialogue_domain",
|
|
|
88 |
"turn_id": datasets.Value("int32"),
|
89 |
"role": datasets.Value("string"),
|
90 |
"da": datasets.Value("string"),
|
91 |
+
"references": [
|
92 |
{
|
93 |
+
"sp_id": datasets.Value("string"),
|
94 |
+
"label": datasets.Value("string"),
|
95 |
}
|
96 |
],
|
97 |
"utterance": datasets.Value("string"),
|
|
|
129 |
features = datasets.Features(
|
130 |
{
|
131 |
"id": datasets.Value("string"),
|
132 |
+
"title": datasets.Value("string"),
|
133 |
+
"context": datasets.Value("string"),
|
134 |
"question": datasets.Value("string"),
|
135 |
"answers": datasets.features.Sequence(
|
136 |
{
|
137 |
"text": datasets.Value("string"),
|
138 |
"answer_start": datasets.Value("int32"),
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
139 |
}
|
140 |
),
|
|
|
|
|
141 |
"domain": datasets.Value("string"),
|
|
|
|
|
142 |
}
|
143 |
)
|
144 |
|
|
|
153 |
def _split_generators(self, dl_manager):
|
154 |
|
155 |
my_urls = _URLs
|
156 |
+
|
157 |
data_dir = dl_manager.download_and_extract(my_urls)
|
158 |
|
159 |
if self.config.name == "dialogue_domain":
|
|
|
161 |
datasets.SplitGenerator(
|
162 |
name=datasets.Split.TRAIN,
|
163 |
gen_kwargs={
|
164 |
+
"filepath": os.path.join(data_dir, "doc2dial/v1.0.1/doc2dial_dial_train.json"),
|
165 |
},
|
166 |
),
|
167 |
datasets.SplitGenerator(
|
168 |
name=datasets.Split.VALIDATION,
|
169 |
gen_kwargs={
|
170 |
+
"filepath": os.path.join(data_dir, "doc2dial/v1.0.1/doc2dial_dial_validation.json"),
|
171 |
},
|
172 |
),
|
173 |
]
|
|
|
176 |
datasets.SplitGenerator(
|
177 |
name=datasets.Split.TRAIN,
|
178 |
gen_kwargs={
|
179 |
+
"filepath": os.path.join(data_dir, "doc2dial/v1.0.1/doc2dial_doc.json"),
|
180 |
},
|
181 |
)
|
182 |
]
|
|
|
185 |
datasets.SplitGenerator(
|
186 |
name=datasets.Split.VALIDATION,
|
187 |
gen_kwargs={
|
188 |
+
"filepath": os.path.join(data_dir, "doc2dial/v1.0.1/doc2dial_dial_validation.json"),
|
|
|
|
|
189 |
},
|
190 |
),
|
191 |
datasets.SplitGenerator(
|
192 |
name=datasets.Split.TRAIN,
|
193 |
gen_kwargs={
|
194 |
+
"filepath": os.path.join(data_dir, "doc2dial/v1.0.1/doc2dial_dial_train.json"),
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
195 |
},
|
196 |
),
|
197 |
]
|
198 |
|
199 |
def _load_doc_data_rc(self, filepath):
|
200 |
+
doc_filepath = os.path.join(os.path.dirname(filepath), "doc2dial_doc.json")
|
201 |
with open(doc_filepath, encoding="utf-8") as f:
|
202 |
data = json.load(f)["doc_data"]
|
203 |
return data
|
204 |
|
205 |
+
def _get_answers_rc(self, references, spans, doc_text):
|
206 |
+
"""Obtain the grounding annotation for a given dialogue turn"""
|
207 |
+
if not references:
|
208 |
+
return []
|
209 |
+
start, end = -1, -1
|
210 |
+
ls_sp = []
|
211 |
+
for ele in references:
|
212 |
+
sp_id = ele["sp_id"]
|
213 |
+
start_sp, end_sp = spans[sp_id]["start_sp"], spans[sp_id]["end_sp"]
|
214 |
+
if start == -1 or start > start_sp:
|
215 |
+
start = start_sp
|
216 |
+
if end < end_sp:
|
217 |
+
end = end_sp
|
218 |
+
ls_sp.append(doc_text[start_sp:end_sp])
|
219 |
+
answer = {
|
220 |
+
"text": doc_text[start:end],
|
221 |
+
"answer_start": start,
|
222 |
+
}
|
223 |
+
return [answer]
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
224 |
|
225 |
def _generate_examples(self, filepath):
|
226 |
"""This function returns the examples in the raw (text) form."""
|
|
|
227 |
if self.config.name == "dialogue_domain":
|
228 |
logging.info("generating examples from = %s", filepath)
|
229 |
with open(filepath, encoding="utf-8") as f:
|
|
|
236 |
"dial_id": dialogue["dial_id"],
|
237 |
"domain": domain,
|
238 |
"doc_id": doc_id,
|
239 |
+
"turns": dialogue["turns"],
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
240 |
}
|
241 |
|
242 |
yield dialogue["dial_id"], x
|
|
|
276 |
"doc_html_ts": data["doc_data"][domain][doc_id]["doc_html_ts"],
|
277 |
"doc_html_raw": data["doc_data"][domain][doc_id]["doc_html_raw"],
|
278 |
}
|
279 |
+
|
280 |
elif self.config.name == "doc2dial_rc":
|
281 |
"""Load dialog data in the reading comprehension task setup, where context is the grounding document,
|
282 |
input query is dialog history in reversed order, and output to predict is the next agent turn."""
|
283 |
|
284 |
logging.info("generating examples from = %s", filepath)
|
|
|
285 |
doc_data = self._load_doc_data_rc(filepath)
|
286 |
with open(filepath, encoding="utf-8") as f:
|
287 |
dial_data = json.load(f)["dial_data"]
|
288 |
for domain, d_doc_dials in dial_data.items():
|
289 |
for doc_id, dials in d_doc_dials.items():
|
290 |
doc = doc_data[domain][doc_id]
|
|
|
|
|
|
|
|
|
291 |
for dial in dials:
|
292 |
all_prev_utterances = []
|
|
|
293 |
for idx, turn in enumerate(dial["turns"]):
|
294 |
+
all_prev_utterances.append("\t{}: {}".format(turn["role"], turn["utterance"]))
|
|
|
|
|
|
|
|
|
|
|
|
|
295 |
if turn["role"] == "agent":
|
296 |
continue
|
297 |
if idx + 1 < len(dial["turns"]):
|
|
|
299 |
turn_to_predict = dial["turns"][idx + 1]
|
300 |
else:
|
301 |
continue
|
302 |
+
question = " ".join(list(reversed(all_prev_utterances))).strip()
|
303 |
+
id_ = "{}_{}".format(dial["dial_id"], turn["turn_id"])
|
304 |
qa = {
|
305 |
"id": id_,
|
|
|
|
|
|
|
|
|
306 |
"title": doc_id,
|
307 |
+
"context": doc["doc_text"],
|
308 |
+
"question": question,
|
309 |
+
"answers": self._get_answers_rc(
|
310 |
+
turn_to_predict["references"],
|
311 |
+
doc["spans"],
|
312 |
+
doc["doc_text"],
|
313 |
+
),
|
314 |
"domain": domain,
|
|
|
|
|
315 |
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
316 |
yield id_, qa
|
dummy/dialogue_domain/{1.1.0 → 1.0.1}/dummy_data.zip
RENAMED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
-
size
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:740c1207d38bd096d900299bb44139a2b858d30ae79154c3d772c0b4a6b4ce05
|
3 |
+
size 13462
|
dummy/{document_domain/1.1.0 → doc2dial_rc/1.0.1}/dummy_data.zip
RENAMED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
-
size
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:740c1207d38bd096d900299bb44139a2b858d30ae79154c3d772c0b4a6b4ce05
|
3 |
+
size 13462
|
dummy/document_domain/1.0.1/dummy_data.zip
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:740c1207d38bd096d900299bb44139a2b858d30ae79154c3d772c0b4a6b4ce05
|
3 |
+
size 13462
|