Unverified Commit 4dfa8aba authored by Lintang Sutawika's avatar Lintang Sutawika Committed by GitHub
Browse files

Merge pull request #682 from yeoedward/anli

[Refactor] Migrate ANLI tasks to yaml
parents 7aa13c47 97a172ae
# Task-name
### Paper
Title: `Adversarial NLI: A New Benchmark for Natural Language Understanding`
Abstract: `https://arxiv.org/pdf/1910.14599.pdf`
Adversarial NLI (ANLI) is a dataset collected via an iterative, adversarial
human-and-model-in-the-loop procedure. It consists of three rounds that progressively
increase in difficulty and complexity, and each question-answer includes annotator-
provided explanations.
Homepage: `https://github.com/facebookresearch/anli`
### Citation
```
@inproceedings{nie-etal-2020-adversarial,
title = "Adversarial {NLI}: A New Benchmark for Natural Language Understanding",
author = "Nie, Yixin and
Williams, Adina and
Dinan, Emily and
Bansal, Mohit and
Weston, Jason and
Kiela, Douwe",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
year = "2020",
publisher = "Association for Computational Linguistics",
}
```
### Subtasks
List or describe tasks defined in this folder, and their names here:
* `anli_r1`: The data collected adversarially in the first round.
* `anli_r2`: The data collected adversarially in the second round, after training on the previous round's data.
* `anli_r3`: The data collected adversarially in the third round, after training on the previous multiple rounds of data.
### Checklist
For adding novel benchmarks/datasets to the library:
* [x] Is the task an existing benchmark in the literature?
* [x] Have you referenced the original paper that introduced the task?
* [ ] If yes, does the original paper provide a reference implementation? If so, have you checked against the reference implementation and documented how to run such a test?
If other tasks on this dataset are already supported:
* [ ] Is the "Main" variant of this task clearly denoted?
* [x] Have you provided a short sentence in a README on what each new variant adds / evaluates?
* [ ] Have you noted which, if any, published evaluation setups are matched by this variant?
group:
- multiple_choice
- natural_language_inference
- nli
- adverserial
task: anli_r1
dataset_path: anli
dataset_name: null
output_type: multiple_choice
training_split: train_r1
validation_split: dev_r1
test_split: test_r1
doc_to_text: "{{premise}}\nQuestion: {{hypothesis}} True, False, or Neither?\nAnswer:"
# True = entailment
# False = contradiction
# Neither = neutral
doc_to_target: "{{['True', 'Neither', 'False'][label]}}"
doc_to_choice:
- "True"
- "Neither"
- "False"
should_decontaminate: true
doc_to_decontamination_query: premise
metric_list:
- metric: acc
aggregation: mean
higher_is_better: true
group:
- multiple_choice
- natural_language_inference
- nli
- adverserial
task: anli_r2
dataset_path: anli
dataset_name: null
output_type: multiple_choice
training_split: train_r2
validation_split: dev_r2
test_split: test_r2
doc_to_text: "{{premise}}\nQuestion: {{hypothesis}} True, False, or Neither?\nAnswer:"
# True = entailment
# False = contradiction
# Neither = neutral
doc_to_target: "{{['True', 'Neither', 'False'][label]}}"
doc_to_choice:
- "True"
- "Neither"
- "False"
should_decontaminate: true
doc_to_decontamination_query: premise
metric_list:
- metric: acc
aggregation: mean
higher_is_better: true
group:
- multiple_choice
- natural_language_inference
- nli
- adverserial
task: anli_r3
dataset_path: anli
dataset_name: null
output_type: multiple_choice
training_split: train_r3
validation_split: dev_r3
test_split: test_r3
doc_to_text: "{{premise}}\nQuestion: {{hypothesis}} True, False, or Neither?\nAnswer:"
# True = entailment
# False = contradiction
# Neither = neutral
doc_to_target: "{{['True', 'Neither', 'False'][label]}}"
doc_to_choice:
- "True"
- "Neither"
- "False"
should_decontaminate: true
doc_to_decontamination_query: premise
metric_list:
- metric: acc
aggregation: mean
higher_is_better: true
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment