Embracing Ambiguity: S hifting the Training Target of NLI Models (2021)

Abstract: Natural Language Inference (NLI) datasets contain examples with highly ambiguous labels. While many research works do not pay much attention to this fact, several recent efforts have been made to acknowledge and embrace the existence of ambiguity, such as UNLI and ChaosNLI. In this paper, we explore the option of training directly on the estimated label distribution of the annotators in the NLI task, using a learning loss based on this ambiguity distribution instead of the gold-labels. We prepare AmbiNLI, a trial dataset obtained from readily available sources, and show it is possible to reduce ChaosNLI divergence scores when finetuning on this data, a promising first step towards learning how to capture linguistic ambiguity. Additionally, we show that training on the same amount of data but targeting the ambiguity distribution instead of gold-labels can result in models that achieve higher performance and learn better representations for downstream tasks.

## introduction ambiguity is intrinsic to natural language, and creating datasets free of this property is a hard if not impossible task. previously, it was common to disregard it as noise or as a sign of poor quality data. more recent research, however, has drawn our attention towards the inevitability of ambiguity, and the necessity to take it into consideration when working on natural language understanding tasks @xcite @xcite . this ambiguity stems from the lack of proper context or differences in background knowledge between annotators, and leads to a large number of examples where the correctness of labels can be debated. chaosnli @xcite ) is a dataset created by manually annotating a subset of the snli @xcite , mnli @xcite , and αnli @xcite datasets. each of the total 4,645 samples received 100 annotations. through this data, they were able to generate a probability distribution over the labels for these samples, which they call the human agreement distribution, with the goal of using it to evaluate the ability of current state-of-the-art models to capture ambiguity. the divergence scores between the model's predicted probability distribution and the true target distribution is computed and compared against random and human baselines. they showed that models trained using gold-labels have very poor performance on the task of capturing the human agreement distribution. although this is a promising first step, it remains unclear how to train models with a better understanding of ambiguity, and what tangible benefits we can obtain when actually doing so. in this work, we study the possibility of shifting the training target of models from gold-labels to the ambiguity distribution, a simple and intuitive yet until now unexplored approach in this domain. we hypothesize that when we finetune a model in this way, we can achieve lower divergence scores in the chaosnli benchmark. further, we believe that it should also bring accuracy improvements in nli and other downstream tasks. the intuition behind our performance expectations is that an ambiguity distribution offers a more informative and less misleading view on the answer to the task, which allows models to learn more from the same data. we prepare a trial dataset with ambiguity distributions obtained from available snli and mnli data, and run experiments to confirm our hypotheses. we refer to it as ambinli, but we do not encourage its use in further work. instead, we encourage the community to follow this direction by performing further data collection in this area. our main contributions are showing that 1) models trained on ambiguity can more closely capture the true human distribution, 2) they are able to attain higher accuracy under otherwise equal conditions, and 3) they learn better representations for downstream tasks. we release the code used for these experiments. 1 2 ambinli ## experiments in our experiments, we use bert-base @xcite with pre-trained weights and a softmax classification head. we use a batch size of 128 and learning rate of 1e-5. learning to capture question ambiguity. in our main experiment, we aim to judge whether is is possible to learn how to capture the human agreement distribution. we first obtain a base model in the same manner as @xcite , by pretraining it for 3 epochs on the gold-labels of the snli and mnli training sets. we observed that this pre-training step is necessary to provide the model with a general understanding of the nli task to compensate for the low amount of ambiguity data available. we then finetune the model on our ambinli 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, measured using the jensen-shannon divergence (jsd), as was done in their original experiments. furthermore, we explore what effect our ambiguity learning has on accuracy by comparing models trained on exactly the same data but with goldlabel training versus ambiguous training. in order to achieve this, we prepare a version of ambinli where we replace the ambiguity distributions with gold-labels. since the two models have seen the exact same data, performance differences can be directly attributed to the process of capturing ambiguity. we report accuracy on chaosnli using their re-computed gold-labels. ## results and discussion training on the ambiguity distribution can reduce divergence scores. unli's skewed distribution worsens scores. when looking at the ambiu and ambismu results in to confirm this, we filtered out all unli samples that had a probability label @xmath0.05 or @xmath1.97, and ran the "filtered" experiments. indeed, in am-biu, this naive filtering process yields about 20% lower jsd scores and about 5% higher accuracy. we conclude that unli data, under the current conversion approach, is somewhat problematic. training on the ambiguity distribution can yield accuracy improvements. we have found that, for the case of ambism, a model trained to target the ambiguity distribution achieves higher accuracy. this means that more precise knowledge can be acquired when learning the true underlying ambiguity of questions instead of the sometimes misleading gold-label. when using unli data (ambiu and ambismu) however, the results are mixed, as discussed above. thus, to further strengthen our argument on the benefit of ambiguity data, we refer to the supplementary experiment results in divergence scores are stable. through the entropy range comparison of ambiguity models learn better representations for transfer learning. lastly, in is ambiguity training worth the extra labeling cost? one argument against this method is the apparent extra labeling cost required. indeed, when comparing the gold-label and ambiguity approaches at equal number of total labels, the goldlabel approach would likely attain higher performance due to the difference in number of samples. however, we argue that collecting multiple labels has several benefits other than ambiguity distribution generation. most importantly, they help avoid mis-labelings and raise the overall quality of the dataset. in many occasions, multiple labels are already being collected for these reasons, but occasionally not released (for example, bowman et al. (2015) didn't release the multiple labels they col- ## conclusion we hypothesized that the intrinsic ambiguity present in natural language datasets can be exploited instead of treating it like noise. we used existing data to generate ambiguity distributions for subsets of snli, mnli, and unli, and trained new models that are capable of more accurately capturing the ambiguity present in these datasets. our results show that it is indeed possible to exploit this ambiguity information, and that for the same amount of data, a model trained to recognize ambiguity shows signs of higher performance in the same task as well as in other downstream tasks. however, our dataset was created using existing resources and lacks in quality and quantity. while it was enough to show that this research direction is promising, it limited the strength of our results. in future work, we wish to obtain larger amounts of data by using crowdsourcing techniques, and expand our scope to other nlp tasks as well.