Adversarial NLI

Paper

Adversarial NLI: A New Benchmark for Natural Language Understanding

Dataset

Version 1.0 is available here: https://dl.fbaipublicfiles.com/anli/anli_v1.0.zip.

Leaderboard

If you want to have your model added to the leaderboard, please reach out to us or submit a PR.

Model	Publication	A1	A2	A3
InfoBERT (RoBERTa Large)	Wang et al., 2020	75.0	50.5	47.7
ALUM (RoBERTa Large)	Liu et al., 2020	72.3	52.1	48.4
GPT-3	Brown et al., 2020	36.8	34.0	40.2
XLNet Large	Yang et al., 2019	70.3	50.9	49.4
RoBERTa Large	Liu et al., 2019	72.4	49.8	44.4

Implementation

To facilitate research in the field of NLI, we provide an easy-to-use codebase for NLI data preparation and modeling. The code is built upon Transformers with a special focus on NLI.

We welcome researchers from various fields (linguistics, machine learning, cognitive science, psychology, etc.) to try NLI. You can use the code to reproduce the results in our paper or even as a starting point for your research.

Please read more in Start your NLI research.

An important detail in our experiments is that we combine SNLI+MNLI+FEVER-NLI and up-sample different rounds of ANLI to train the models.
We highly recommend you refer to the above link for reproducing the results and training your models such that the results will be comparable to the ones on the leaderboard.

Pre-trained Models

Pre-trained NLI models can be easily called through huggingface model hub.

Models: RoBERTa, ALBert, BART, ELECTRA, XLNet.

The training data is a combination of SNLI, MNLI, FEVER-NLI, ANLI (R1, R2, R3). Please also cite the datasets if you are using the pre-trained model.

Please try the code snippet below.

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

if __name__ == '__main__':
    max_length = 256

    premise = "Two women are embracing while holding to go packages."
    hypothesis = "The men are fighting outside a deli."

    hg_model_hub_name = "ynie/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli"
    # hg_model_hub_name = "ynie/albert-xxlarge-v2-snli_mnli_fever_anli_R1_R2_R3-nli"
    # hg_model_hub_name = "ynie/bart-large-snli_mnli_fever_anli_R1_R2_R3-nli"
    # hg_model_hub_name = "ynie/electra-large-discriminator-snli_mnli_fever_anli_R1_R2_R3-nli"
    # hg_model_hub_name = "ynie/xlnet-large-cased-snli_mnli_fever_anli_R1_R2_R3-nli"

    tokenizer = AutoTokenizer.from_pretrained(hg_model_hub_name)
    model = AutoModelForSequenceClassification.from_pretrained(hg_model_hub_name)

    tokenized_input_seq_pair = tokenizer.encode_plus(premise, hypothesis,
                                                     max_length=max_length,
                                                     return_token_type_ids=True, truncation=True)

    input_ids = torch.Tensor(tokenized_input_seq_pair['input_ids']).long().unsqueeze(0)
    # remember bart doesn't have 'token_type_ids', remove the line below if you are using bart.
    token_type_ids = torch.Tensor(tokenized_input_seq_pair['token_type_ids']).long().unsqueeze(0)
    attention_mask = torch.Tensor(tokenized_input_seq_pair['attention_mask']).long().unsqueeze(0)

    outputs = model(input_ids,
                    attention_mask=attention_mask,
                    token_type_ids=token_type_ids,
                    labels=None)
    # Note:
    # "id2label": {
    #     "0": "entailment",
    #     "1": "neutral",
    #     "2": "contradiction"
    # },

    predicted_probability = torch.softmax(outputs[0], dim=1)[0].tolist()  # batch_size only one

    print("Premise:", premise)
    print("Hypothesis:", hypothesis)
    print("Entailment:", predicted_probability[0])
    print("Neutral:", predicted_probability[1])
    print("Contradiction:", predicted_probability[2])

Rules

When using this dataset, we ask that you obey some very simple rules:

We want to make it easy for people to provide ablations on test sets without being rate limited, so we release labeled test sets with this distribution. We trust that you will act in good faith, and will not tune on the test set (this should really go without saying)! We may release unlabeled test sets later.
Training data is for training, development data is for development, and test data is for reporting test numbers. This means that you should not e.g. train on the train+dev data from rounds 1 and 2 and then report an increase in performance on the test set of round 3.
We will host a leaderboard on this page. If you want to be added to the leaderboard, please contact us and/or submit a PR with a link to your paper, a link to your code in a public repository (e.g. Github), together with the following information: number of parameters in your model, data used for (pre-)training, and your dev and test results for each round, as well as the total over all rounds.

Reason

AdversarialNLI dataset contains a reason field for each examples in the dev and test split and for some examples in the train split. The reason is collected by asking annotator "Why do you think this example is hard?".

Other NLI Reference

We used following NLI resources in training the backend model of the adversarial collection:

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",
}

License

ANLI is licensed under Creative Commons-Non Commercial 4.0. See the LICENSE file for details.

yicheng-w/anli