Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations in a Label-Abundant Setup (2022)

Abstract: Training a model to provide natural language explanations (NLEs) for its predictions usually requires the acquisition of task-specific NLEs, which is time- and resource-consuming. A potential solution is the few-shot out-of-domain transfer of NLEs from a parent task with many NLEs to a child task.In this work, we examine the setup in which the child task has few NLEs but abundant labels. We establish four few-shot transfer learning methods that cover the possible fine-tuning combinations of the labels and NLEs for the parent and child tasks. We transfer explainability from a large natural language inference dataset (e-SNLI) separately to two child tasks: (1) hard cases of pronoun resolution, where we introduce the small-e-WinoGrande dataset of NLEs on top of the WinoGrande dataset, and (2) commonsense validation (ComVE). Our results demonstrate that the parent task helps with NLE generation and we establish the best methods for this setup.

## introduction recent developments have made it possible for ai models to learn from natural language explanations (nles) for the ground-truth labels at training time and generate such explanations for their decisions at deployment time @xcite @xcite @xcite @xcite @xcite . however, large datasets of nles, such as e-snli @xcite , are time-consuming and expensive to gather. one approach is to transfer explanations from a different domain, via few-shot transfer learning. the usual setup for few-shot out-of-domain transfer learning consists of transfer learning from a "parent" task, with abundant training examples, to a "child" task that only has a few training examples @xcite . in this work, we assume that the child task has few training nles but abundant labels. given the advent of deep learning in the last years, this scenario may be quite frequent, as one may already have a large dataset with labels on which they aim to train nles-generating models without annotating the entire dataset with nles. to our knowledge, there is only one existing work in this setup, that of @xcite , which introduces a vanilla fine-tuning method on top of the zero-shot wt5 model @xcite . however, their work is limited by: (1) they only test one of the four possible scenarios we identify for this setup, and (2) they use only automatic evaluation metrics, which do not necessarily align with human judgment @xcite . in this work, we introduce three few-shot transfer learning methods for nles that utilize the abundant training labels for both the parent and child task, and we adapt for computational efficiency the method from @xcite . together, these four methods are combinations of multi-task learning and fine-tuning between a parent and a child task with few training nles but abundant labels. we instantiate our few-shot learning approaches on e-snli @xcite as parent task, and winogrande @xcite and comve @xcite as child tasks. as the winogrande dataset does not contain nles, we introduce small-e-winogrande, which provides 100/50/100 nles for the training, development, and test sets, respectively. we show the extent to which few-shot out-of-domain transfer learning of nles is currently feasible, and provide insight into which learning techniques work best in this setup. we perform human evaluation and compare against child-task-only and zero-shot baselines. winogrande. the winogrande dataset (sakaguchi et al., 2020) consists of 40,398 binary fillin-the-gap instances of pronoun resolution that follow the winograd schema format @xcite . we select winogrande as a child task, since it requires implicit knowledge, which we want to capture in the nles. we construct the small-e-winogrande dataset by manually creating nles for 100/50/100 training/dev/test instances. ## results following @xcite , we use an aggregated score (we call "nle score") of the four categories (yes, weak yes, no, weak no) to compare the nle generation quality, where yes, weak yes, weak no, and no are given the weights 1, 2/3, 1/3, and 0, respectively. this aggregation has two goals: (1) to provide a single metric to compare the methods, and (2) to account for the subjective nature of choosing between close labels such as yes and weak yes. a summary of the yes, weak yes, weak no, and no scores and the shortcomings are presented in appendix d. for every model comparison, we report if it is statistically significant via the paired student's t-test for equal variances @xcite , with single-tailed p-values and 0.05 statistical significance threshold. the results are given in first, we notice that all methods (m1-m4) significantly outperform the zero-shot baselines (wt5fine-tune and wt5) in terms of nle quality for both datasets, which proves the utility of the 50 child-task nles. second, we see that not all methods outperform the child-task baselines. for example, on wino-grande, both cd-fine-tune and cd-union outper-form m1 and m3 in terms of the nle quality. this shows that it is sometimes possible that fine-tuning on a large parent task of out-of-domain nles hurts nle quality of a child task. however, for both datasets, the best performing method is among the m1-m4 methods (and for comve, all m1-m4 methods outperform the child-task baselines), suggesting that it is generally useful to use a large dataset of nles as a parent task even when out-ofdomain. third, we see that the m1-m4 methods rank differently on different datasets, in particular, m2 and m4 are the significantly best methods on wino-grande, and m4 is the significantly best method on comve. we believe that the main difference in method ranking is that the methods obtain much closer-to-chance accuracy on winogrande than on comve. this is important, because poor task understanding can lead to poor nle generation. in particular, m2 and m4 obtain significantly better winogrande accuracy than m1 and m3, which might explain the significant gap in nle score. m2 and m4 are the best nle generation methods on both datasets, which could be because m2 and m4 use the 50 nles of the child task in a separate training regime, whereas m1 and m3 use a combination of the much larger child dataset with the 50 nles. this suggests that the 50 nles require their own training regime to obtain good nle generation. finally, we see that the best task performances on both datasets are obtained by a baseline method: wt5-fine-tune (no child nles) for winogrande and cd-fine-tune (child-task only) for comve. moreover, among the m1-m4 methods, there is no clear best method in terms of task performance, nor a correlation with the nle quality. hence, a tradeoff between task performance and nle quality is needed, which we observe is achieved by m4. ## related work there are three main focuses in nle generation: quality improvement @xcite @xcite , nle faithfulness @xcite @xcite , and transfer learning of nles. zero-shot indomain transfer of nles (between datasets of the same task) has been done, e.g., by @xcite ; @xcite @xcite . @xcite additionally consider zero-shot out-of-domain transfer of nles, while erliksson et al. (2021) extend their work by showing that few-shot out-of-domain transfer of nles is possible in the abundant-label setup. @xcite use prompt engineering for few-shot out-of-domain transfer of nles for the scarce-label setup. the prompt choice is less relevant in our abundant-label setup, because task adaptation can be done via the abundant training labels. in the more general area of natural language generation, few-shot learning is a growing topic @xcite , e.g., in dialog generation @xcite . these approaches, however, do not directly apply to transfer learning of nles, which is a dual task of predicting both the label and generating an explanation. ## summary and outlook in this work, we investigated four methods for fewshot out-of-domain transfer learning of nles for the abundant-label setting. we introduced small-e-winogrande, a dataset of nles on top of a small sample of instances from winogrande. we showed that out-of-domain few-shot learning can significantly help with nle generation compared to zeroshot or child-task-only learning. amongst the four nle few-shot learning methods, we found that the most convincing nles are generated by the methods that provide separate training regimes for the child task and its few training nles. while our results indicate that few-shot out-of-domain transfer learning of nles is helpful, there is room for improvement both in the quality of the generated nles and in task-performance. thus, our work provides an essential foundation for future research into few-shot out-of-domain transfer learning of nles where label abundance is available.