|
--- |
|
annotations_creators: |
|
aidayago2: |
|
- crowdsourced |
|
- found |
|
- machine-generated |
|
cweb: |
|
- crowdsourced |
|
- found |
|
- machine-generated |
|
eli5: |
|
- crowdsourced |
|
- found |
|
fever: |
|
- crowdsourced |
|
- found |
|
- machine-generated |
|
hotpotqa: |
|
- crowdsourced |
|
- found |
|
- machine-generated |
|
nq: |
|
- crowdsourced |
|
- found |
|
- machine-generated |
|
structured_zeroshot: |
|
- crowdsourced |
|
- found |
|
- machine-generated |
|
trex: |
|
- crowdsourced |
|
- found |
|
- machine-generated |
|
triviaqa_support_only: |
|
- crowdsourced |
|
- found |
|
- machine-generated |
|
wned: |
|
- crowdsourced |
|
- found |
|
- machine-generated |
|
wow: |
|
- crowdsourced |
|
- found |
|
- machine-generated |
|
language_creators: |
|
aidayago2: |
|
- crowdsourced |
|
cweb: |
|
- crowdsourced |
|
eli5: |
|
- found |
|
fever: |
|
- crowdsourced |
|
hotpotqa: |
|
- crowdsourced |
|
- found |
|
nq: |
|
- found |
|
structured_zeroshot: |
|
- crowdsourced |
|
trex: |
|
- crowdsourced |
|
triviaqa_support_only: |
|
- found |
|
wned: |
|
- crowdsourced |
|
wow: |
|
- crowdsourced |
|
languages: |
|
- en |
|
licenses: |
|
- mit |
|
multilinguality: |
|
- monolingual |
|
size_categories: |
|
aidayago2: |
|
- 10K<n<100K |
|
cweb: |
|
- 10K<n<100K |
|
eli5: |
|
- 100K<n<1M |
|
fever: |
|
- 100K<n<1M |
|
hotpotqa: |
|
- 100K<n<1M |
|
nq: |
|
- 10K<n<100K |
|
structured_zeroshot: |
|
- 100K<n<1M |
|
trex: |
|
- 1M<n<10M |
|
triviaqa_support_only: |
|
- 10K<n<100K |
|
wned: |
|
- 1K<n<10K |
|
wow: |
|
- 100K<n<1M |
|
source_datasets: |
|
aidayago2: |
|
- extended|other-aidayago |
|
- original |
|
cweb: |
|
- extended|other-wned-cweb |
|
- original |
|
eli5: |
|
- extended|other-hotpotqa |
|
- original |
|
fever: |
|
- extended|other-fever |
|
- original |
|
hotpotqa: |
|
- extended|other-hotpotqa |
|
- original |
|
nq: |
|
- extended|natural_questions |
|
- original |
|
structured_zeroshot: |
|
- extended|other-zero-shot-re |
|
- original |
|
trex: |
|
- extended|other-trex |
|
- original |
|
triviaqa_support_only: |
|
- extended|other-triviaqa |
|
- original |
|
wned: |
|
- extended|other-wned-wiki |
|
- original |
|
wow: |
|
- extended|other-wizardsofwikipedia |
|
- original |
|
task_categories: |
|
aidayago2: |
|
- text-retrieval |
|
cweb: |
|
- text-retrieval |
|
eli5: |
|
- question-answering |
|
- text-retrieval |
|
fever: |
|
- text-classification |
|
- text-retrieval |
|
hotpotqa: |
|
- question-answering |
|
- text-retrieval |
|
nq: |
|
- question-answering |
|
- text-retrieval |
|
structured_zeroshot: |
|
- sequence-modeling |
|
- text-retrieval |
|
trex: |
|
- sequence-modeling |
|
- text-retrieval |
|
triviaqa_support_only: |
|
- question-answering |
|
- text-retrieval |
|
wned: |
|
- text-retrieval |
|
wow: |
|
- sequence-modeling |
|
- text-retrieval |
|
task_ids: |
|
aidayago2: |
|
- document-retrieval |
|
- entity-linking-retrieval |
|
cweb: |
|
- document-retrieval |
|
- entity-linking-retrieval |
|
eli5: |
|
- abstractive-qa |
|
- document-retrieval |
|
- open-domain-qa |
|
fever: |
|
- document-retrieval |
|
- fact-checking |
|
- fact-checking-retrieval |
|
hotpotqa: |
|
- document-retrieval |
|
- extractive-qa |
|
- open-domain-qa |
|
nq: |
|
- document-retrieval |
|
- extractive-qa |
|
- open-domain-qa |
|
structured_zeroshot: |
|
- document-retrieval |
|
- slot-filling |
|
trex: |
|
- document-retrieval |
|
- slot-filling |
|
triviaqa_support_only: |
|
- document-retrieval |
|
- extractive-qa |
|
- open-domain-qa |
|
wned: |
|
- document-retrieval |
|
- entity-linking-retrieval |
|
wow: |
|
- dialogue-modeling |
|
- document-retrieval |
|
--- |
|
|
|
# Dataset Card for KILT |
|
|
|
## Table of Contents |
|
- [Dataset Description](#dataset-description) |
|
- [Dataset Summary](#dataset-summary) |
|
- [Supported Tasks](#supported-tasks-and-leaderboards) |
|
- [Languages](#languages) |
|
- [Dataset Structure](#dataset-structure) |
|
- [Data Instances](#data-instances) |
|
- [Data Fields](#data-instances) |
|
- [Data Splits](#data-instances) |
|
- [Dataset Creation](#dataset-creation) |
|
- [Curation Rationale](#curation-rationale) |
|
- [Source Data](#source-data) |
|
- [Annotations](#annotations) |
|
- [Personal and Sensitive Information](#personal-and-sensitive-information) |
|
- [Considerations for Using the Data](#considerations-for-using-the-data) |
|
- [Social Impact of Dataset](#social-impact-of-dataset) |
|
- [Discussion of Biases](#discussion-of-biases) |
|
- [Other Known Limitations](#other-known-limitations) |
|
- [Additional Information](#additional-information) |
|
- [Dataset Curators](#dataset-curators) |
|
- [Licensing Information](#licensing-information) |
|
- [Citation Information](#citation-information) |
|
- [Contributions](#contributions) |
|
|
|
## Dataset Description |
|
|
|
- **Homepage:** https://ai.facebook.com/tools/kilt/ |
|
- **Repository:** https://github.com/facebookresearch/KILT |
|
- **Paper:** https://arxiv.org/abs/2009.02252 |
|
- **Leaderboard:** https://eval.ai/web/challenges/challenge-page/689/leaderboard/ |
|
- **Point of Contact:** [Needs More Information] |
|
|
|
### Dataset Summary |
|
|
|
KILT has been built from 11 datasets representing 5 types of tasks: |
|
|
|
- Fact-checking |
|
- Entity linking |
|
- Slot filling |
|
- Open domain QA |
|
- Dialog generation |
|
|
|
All these datasets have been grounded in a single pre-processed Wikipedia dump, allowing for fairer and more consistent evaluation as well as enabling new task setups such as multitask and transfer learning with minimal effort. KILT also provides tools to analyze and understand the predictions made by models, as well as the evidence they provide for their predictions. |
|
|
|
#### Loading the KILT knowledge source and task data |
|
|
|
The original KILT [release](https://github.com/facebookresearch/KILT) only provides question IDs for the TriviaQA task. Using the full dataset requires mapping those back to the TriviaQA questions, which can be done as follows: |
|
|
|
```python |
|
from datasets import load_dataset |
|
|
|
# Get the pre-processed Wikipedia knowledge source for kild |
|
kilt_wiki = load_dataset("kilt_wikipedia") |
|
|
|
# Get the KILT task datasets |
|
kilt_triviaqa = load_dataset("kilt_tasks", name="triviaqa_support_only") |
|
|
|
# Most tasks in KILT already have all required data, but KILT-TriviaQA |
|
# only provides the question IDs, not the questions themselves. |
|
# Thankfully, we can get the original TriviaQA data with: |
|
trivia_qa = load_dataset('trivia_qa', 'unfiltered.nocontext') |
|
|
|
# The KILT IDs can then be mapped to the TriviaQA questions with: |
|
triviaqa_map = {} |
|
for k in ['train', 'validation', 'test']: |
|
triviaqa_map = dict([(q_id, i) for i, q_id in enumerate(trivia_qa[k]['question_id'])]) |
|
kilt_triviaqa[k] = kilt_triviaqa[k].filter(lambda x: x['id'] in triviaqa_map) |
|
kilt_triviaqa[k] = kilt_triviaqa[k].map(lambda x: {'input': trivia_qa[k][triviaqa_map[x['id']]]['question']}) |
|
``` |
|
|
|
### Supported Tasks and Leaderboards |
|
|
|
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia. |
|
|
|
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/). |
|
|
|
### Languages |
|
|
|
All tasks are in English (`en`). |
|
|
|
## Dataset Structure |
|
|
|
### Data Instances |
|
|
|
An example of open-domain QA from the Natural Questions `nq` configuration looks as follows: |
|
``` |
|
{'id': '-5004457603684974952', |
|
'input': 'who is playing the halftime show at super bowl 2016', |
|
'meta': {'left_context': '', |
|
'mention': '', |
|
'obj_surface': [], |
|
'partial_evidence': [], |
|
'right_context': '', |
|
'sub_surface': [], |
|
'subj_aliases': [], |
|
'template_questions': []}, |
|
'output': [{'answer': 'Coldplay', |
|
'meta': {'score': 0}, |
|
'provenance': [{'bleu_score': 1.0, |
|
'end_character': 186, |
|
'end_paragraph_id': 1, |
|
'meta': {'annotation_id': '-1', |
|
'evidence_span': [], |
|
'fever_page_id': '', |
|
'fever_sentence_id': -1, |
|
'yes_no_answer': ''}, |
|
'section': 'Section::::Abstract.', |
|
'start_character': 178, |
|
'start_paragraph_id': 1, |
|
'title': 'Super Bowl 50 halftime show', |
|
'wikipedia_id': '45267196'}]}, |
|
{'answer': 'Beyoncé', |
|
'meta': {'score': 0}, |
|
'provenance': [{'bleu_score': 1.0, |
|
'end_character': 224, |
|
'end_paragraph_id': 1, |
|
'meta': {'annotation_id': '-1', |
|
'evidence_span': [], |
|
'fever_page_id': '', |
|
'fever_sentence_id': -1, |
|
'yes_no_answer': ''}, |
|
'section': 'Section::::Abstract.', |
|
'start_character': 217, |
|
'start_paragraph_id': 1, |
|
'title': 'Super Bowl 50 halftime show', |
|
'wikipedia_id': '45267196'}]}, |
|
{'answer': 'Bruno Mars', |
|
'meta': {'score': 0}, |
|
'provenance': [{'bleu_score': 1.0, |
|
'end_character': 239, |
|
'end_paragraph_id': 1, |
|
'meta': {'annotation_id': '-1', |
|
'evidence_span': [], |
|
'fever_page_id': '', |
|
'fever_sentence_id': -1, |
|
'yes_no_answer': ''}, |
|
'section': 'Section::::Abstract.', |
|
'start_character': 229, |
|
'start_paragraph_id': 1, |
|
'title': 'Super Bowl 50 halftime show', |
|
'wikipedia_id': '45267196'}]}, |
|
{'answer': 'Coldplay with special guest performers Beyoncé and Bruno Mars', |
|
'meta': {'score': 0}, |
|
'provenance': []}, |
|
{'answer': 'British rock group Coldplay with special guest performers Beyoncé and Bruno Mars', |
|
'meta': {'score': 0}, |
|
'provenance': []}, |
|
{'answer': '', |
|
'meta': {'score': 0}, |
|
'provenance': [{'bleu_score': 0.9657992720603943, |
|
'end_character': 341, |
|
'end_paragraph_id': 1, |
|
'meta': {'annotation_id': '2430977867500315580', |
|
'evidence_span': [], |
|
'fever_page_id': '', |
|
'fever_sentence_id': -1, |
|
'yes_no_answer': 'NONE'}, |
|
'section': 'Section::::Abstract.', |
|
'start_character': 0, |
|
'start_paragraph_id': 1, |
|
'title': 'Super Bowl 50 halftime show', |
|
'wikipedia_id': '45267196'}]}, |
|
{'answer': '', |
|
'meta': {'score': 0}, |
|
'provenance': [{'bleu_score': -1.0, |
|
'end_character': -1, |
|
'end_paragraph_id': 1, |
|
'meta': {'annotation_id': '-1', |
|
'evidence_span': ['It was headlined by the British rock group Coldplay with special guest performers Beyoncé and Bruno Mars', |
|
'It was headlined by the British rock group Coldplay with special guest performers Beyoncé and Bruno Mars, who previously had headlined the Super Bowl XLVII and Super Bowl XLVIII halftime shows, respectively.', |
|
"The Super Bowl 50 Halftime Show took place on February 7, 2016, at Levi's Stadium in Santa Clara, California as part of Super Bowl 50. It was headlined by the British rock group Coldplay with special guest performers Beyoncé and Bruno Mars", |
|
"The Super Bowl 50 Halftime Show took place on February 7, 2016, at Levi's Stadium in Santa Clara, California as part of Super Bowl 50. It was headlined by the British rock group Coldplay with special guest performers Beyoncé and Bruno Mars,"], |
|
'fever_page_id': '', |
|
'fever_sentence_id': -1, |
|
'yes_no_answer': ''}, |
|
'section': 'Section::::Abstract.', |
|
'start_character': -1, |
|
'start_paragraph_id': 1, |
|
'title': 'Super Bowl 50 halftime show', |
|
'wikipedia_id': '45267196'}]}]} |
|
``` |
|
|
|
### Data Fields |
|
|
|
Examples from all configurations have the following features: |
|
|
|
- `input`: a `string` feature representing the query. |
|
- `output`: a `list` of features each containing information for an answer, made up of: |
|
- `answer`: a `string` feature representing a possible answer. |
|
- `provenance`: a `list` of features representing Wikipedia passages that support the `answer`, denoted by: |
|
- `title`: a `string` feature, the title of the Wikipedia article the passage was retrieved from. |
|
- `section`: a `string` feature, the title of the section in Wikipedia article. |
|
- `wikipedia_id`: a `string` feature, a unique identifier for the Wikipedia article. |
|
- `start_character`: a `int32` feature. |
|
- `start_paragraph_id`: a `int32` feature. |
|
- `end_character`: a `int32` feature. |
|
- `end_paragraph_id`: a `int32` feature. |
|
|
|
|
|
### Data Splits |
|
|
|
[Needs More Information] |
|
|
|
## Dataset Creation |
|
|
|
### Curation Rationale |
|
|
|
[Needs More Information] |
|
|
|
### Source Data |
|
|
|
#### Initial Data Collection and Normalization |
|
|
|
[Needs More Information] |
|
|
|
#### Who are the source language producers? |
|
|
|
[Needs More Information] |
|
|
|
### Annotations |
|
|
|
#### Annotation process |
|
|
|
[Needs More Information] |
|
|
|
#### Who are the annotators? |
|
|
|
[Needs More Information] |
|
|
|
### Personal and Sensitive Information |
|
|
|
[Needs More Information] |
|
|
|
## Considerations for Using the Data |
|
|
|
### Social Impact of Dataset |
|
|
|
[Needs More Information] |
|
|
|
### Discussion of Biases |
|
|
|
[Needs More Information] |
|
|
|
### Other Known Limitations |
|
|
|
[Needs More Information] |
|
|
|
## Additional Information |
|
|
|
### Dataset Curators |
|
|
|
[Needs More Information] |
|
|
|
### Licensing Information |
|
|
|
[Needs More Information] |
|
|
|
### Citation Information |
|
|
|
[Needs More Information] |
|
|
|
### Contributions |
|
|
|
Thanks to [@thomwolf](https://github.com/thomwolf), [@yjernite](https://github.com/yjernite) for adding this dataset. |
