Capture Human Disagreement Distributions by Calibrated Networks for Natural Language Inference (2022)

Abstract: Natural Language Inference (NLI) datasets contain examples with highly ambiguous labels due to its subjectivity. Several recent efforts have been made to acknowledge and embrace the existence of ambiguity, and explore how to capture the human disagreement distribution. In contrast with directly learning from gold ambiguity labels, relying on special resource, we argue that the model has naturally captured the human ambiguity distribution as long as it’s calibrated, i.e. the predictive probability can reflect the true correctness likelihood. Our experiments show that when model is well-calibrated, either by label smoothing or temperature scaling, it can obtain competitive performance as prior work, on both divergence scores between predictive probability and the true human opinion distribution, and the accuracy. This reveals the overhead of collecting gold ambiguity labels can be cut, by broadly solving how to calibrate the NLI network.

## introduction ambiguity is intrinsic to natural language. previously, it's common to disregard it as noise or as a sign of poor-quality data, because we implicitly make the assumption that there is only one correct label given an example, indicating the unique class it belongs to. however, it is against the subjectivity of many natural language understanding (nlu) tasks, such as natural language inference (nli) and semantic textual similarity (sts), as their annotations are heavily based on personal experience and opinions. more recent research has gravitated towards the necessity to acknowledge and embrace the existence of ambiguity in nli. @xcite shows that human disagreements, very often, are not dismissible as annotation noise, but rather persist as collecting more ratings and varying the amount of context * * work carried out as an intern at huawei 2012 lab. ## hypothesis while softmax of nli models trained with hard labels allows the model to represent predictive confidence, this probability does not necessarily mimic the uncertainty that exists among humans' perceptions @xcite . we speculate targeting one-hot labels leads model to be overconfident. the miscalibration of over-confidence results in the disability to represent human disagreement distributions correctly. meissner et al. ( 2021 ) explores to train on the empirically-gold soft labels collected by crowdsourcing annotations. they find training on the same amount of data but targeting the ambiguity distribution instead of hard labels can reduce chaosnli divergence scores (jsd) and achieve higher performance. so they advocate to use crowdsourcing techniques to obtain a label distribution by collecting multiple annotations given an instance, instead of only one as before. however, is the success completely attributed to the usage of empirically-gold label distribution as training target? what essentially makes difference? in our view, it's the soft label -output of label-smoothing, as an effective technique on recalibration, regularisation and loss correction that plays an important role in this success. the ambiguity distribution they employed is just a special soft label, targeting other label smoothing outputs is believed to have comparable performance, but much cheaper than crowdsourcing distribution. moreover, we argue that even training with gold soft labels as ambinli @xcite , cannot always obtain improvements. it may bring degradation when model has been under-confident or calibrated, as continuous training with soft labels will exacerbate under-confidence, which deviates prediction away from the correct one. besides, ambinli only showed performance on one special benchmark -chaosnli which concentrates on ambiguous cases. how about performance on other corpus that consist of both ambiguous and extreme non-ambiguous instances? therefore, we posit that the linguistic ambiguity have been learned by models that is well-calibrated, even if just trained on previous one-hot labels. and not only label-smoothing, but other re-calibration approaches can reach the same goal. so we conduct a case study to explore: 1) can other soft labels generated from the label smoothing achieve competitive results as crowdsourcing ambiguity distribution? 2) is training using soft labels always better than using one-hot labels? and vice versa? 3) without soft labels, can other recalibration methods narrow the distance to true correctness probability either? ## dataset and metric this section gives descriptions of datasets throughout this work, and metrics to assess predictions. ## case study this section empirically verifies our hypotheses. experimental setup for fair comparison, we follow the setup of ambinli @xcite , and reproduce the experiments completely. we use bert-base @xcite with pre-trained weights and a softmax classification head. we use a batch size of 64 and learning rate of 1e-5. 3 we first obtain a base-nli model by pre-training 3 epochs on the gold-labels of the combination of snli and mnli training sets. meissner et al. observed that this pre-training step is necessary to provide the model with a general understanding of the nli task. we then finetune the model on other nli dataset, setting the training objective to be the minimization of the cross-entropy between the output probability distribution and the target ambiguity distribution. for evaluation, we compute the chaosnli divergence scores, calibration error and sharpness. the reproduced results are closely similar to the original paper shown in ## domain transfer it's well-known that accuracy will drop in domain transfer. but how about jsd and ece? is calibration an intrinsic property of the model which is independent of evaluation benchmark? can knowledge of linguistic ambiguity transferred across domains? can the property of calibration learned from general domain transfer to the biomedical? to observe how accuracy, jsd and ece varies in domain transfer, we first evaluate ambisnli, ambimnli and mednli test set, under three nli models: snli, mnli and smnli trained using snli, mnli training set and their combinations resp. evaluation of snli model on ambimnli is across textual genres, same as mnli model on ambisnli. all models on mednli is across-domain, contrasting with in-domain evaluation -snli and smnli on ambisnli, mnli and smnli on ambimnli. metrics of jsd and ece are data-dependent as accuracy. the knowledge of linguistic ambiguity learned from general-purpose domain can be transferred to the medical. in middle of ## conclusion in this paper, we explore how to capture the human linguistic ambiguity from the perspective of model calibration. we empirically verify our hypothesis that nli models have naturally captured the linguistic ambiguity as long as it's well-calibrated. in such case, model predictions can truly reflect the correct human subjective distribution. moreover, we find it's not always better to train with soft labels than hard ones, particularly in highly-certain data cluster. and the knowledge of linguistic ambiguity can be transferred across domains, benefiting lowresource setups. these takeaways are significant for future work in ambiguous nli. we further confirm this by randomly select (rs) subset of ambiu to ambism, i.e. am-bismu rs32924 foot_5 , ambismu rs18000, am-bismu rs9000, the number behind sr represents the size of the subset, we can see that the less am-biu is incorporated, the better scores can be gained. thus, unli data, under the current conversion approach, is somewhat problematic. we only apply ambisnli and ambimnli in the experiments.