Generative Data Augmentation for Commonsense Reasoning (2020)

Abstract: Recent advances in commonsense reasoning depend on large-scale human-annotated training sets to achieve peak performance. However, manual curation of training sets is expensive and has been shown to introduce annotation artifacts that neural models can readily exploit and overfit to. We propose a novel generative data augmentation technique, G-DAUGˆC, that aims to achieve more accurate and robust learning in a low-resource setting. Our approach generates synthetic examples using pretrained language models and selects the most informative and diverse set of examples for data augmentation. On experiments with multiple commonsense reasoning benchmarks, G-DAUGˆC consistently outperforms existing data augmentation methods based on back-translation, establishing a new state-of-the-art on WinoGrande, CODAH, and CommonsenseQA, as well as enhances out-of-distribution generalization, proving to be robust against adversaries or perturbations. Our analysis demonstrates that G-DAUGˆC produces a diverse set of fluent training examples, and that its selection and training approaches are important for performance.

## introduction while recent advances in large-scale neural language models @xcite @xcite have led to strong performance on several commonsense reasoning benchmarks @xcite @xcite , their accuracy by and large depends on the availability of large-scale human-authored training data. however, crowdsourcing examples at scale for each new task and domain can be prohibitively expensive. moreover, human-authored data has been shown to exhibit annotation artifacts @xcite figure 1 : example of a selected high-quality generated example compared to a human-authored example from the winogrande dataset. composing commonsense questions can require creativity. @xcite , leading to models with considerably weaker performance on outof-distribution samples @xcite @xcite . a candidate solution that has shown promise in other tasks, such as reading comprehension, is to augment a human-authored training set with a large set of synthetically-generated examples @xcite @xcite . but, generating synthetic examples for commonsense reasoning poses a unique challenge. in reading comprehension, for instance, the goal of data augmentation is to generate questions that are directly answerable by a given reference passage. in contrast, answering commonsense questions relies on commonsense notions that are seldom stated explicitly @xcite , and authoring such questions can require creativity (see figure 1 ). based on promising evidence from previous work @xcite @xcite , we hypothesize that pretrained language models, such as gpt-2 @xcite , capture some common sense expressed implicitly in their pretraining corpus. could questions generated by such models serve as helpful training data? in this work, we explore this question through generative data augmentation for commonsense reasoning (g-daug c ; §2): a novel framework for augmenting training data with diverse and informative synthetic training examples to improve both in-distribution performance and out-of-distribution generalization of commonsense reasoning models. 1 although a generative model allows us to produce large pools of synthetic training examples, the generated examples may be noisy or redundant. to ensure that we use the most informative examples for augmentation, we introduce data selection methods based on influence functions @xcite and a heuristic to maximize the diversity of the generated data pool. finally, we propose an effective two-stage training scheme for augmentation with synthetic data. in experiments across multiple commonsense benchmarks, we show that g-daug c can mitigate the expense and brittleness resulting from large training sets for commonsense reasoning tasks. to summarize, our contributions include: 1. g-daug c , a generative data augmentation framework for commonsense reasoning ( §2), 2. novel selection methods that identify informative and diverse synthetic training examples from the generated pool ( §3), 3. experiments showing that g-daug c improves in-distribution performance, achieving a 1-4% average absolute gain across four commonsense reasoning data sets and state-of-theart results on the winogrande @xcite , commonsenseqa @xcite , and codah @xcite benchmarks, and also improves model robustness in terms of resistance to adversarial attacks @xcite and accuracy on perturbed evaluation sets ( §4), and 4. a comprehensive analysis of the factors that influence g-daug c 's performance ( §5). ## g-daug c we now describe our framework for generative data augmentation for commonsense reasoning (g-daug c ). figure 2 shows an overview of the approach. we describe g-daug c 's data generation procedure (steps 1 and 2 in the figure) in this section, and cover the data selection and training components (steps 3-5) in §3. ## synthetic data selection and training the above generation method can produce a large pool of examples, but training on all of them would be computationally expensive and might harm performance due to noisy generations. here, we propose three data selection methods aimed at choosing more effective training examples from the generated pool ( §3.1). further, we outline a simple staged training procedure ( §3.2) to mitigate the negative impact from noise in the synthetic data. ## experiments we present experiments on four commonsense multiple choice qa benchmarks: commonsenseqa @xcite , winogrande @xcite , codah @xcite and hel-laswag @xcite . our techniques are also directly applicable to other closed-book multiple choice qa setups, such as science qa, and to textual entailment tasks with minor modifications. to evaluate g-daug c 's extensibility to these settings, we also experiment with a textual entailment task, snli @xcite , and a closedbook version of the arc-challenge scientific qa task @xcite in which access to the scientific corpus for the arc dataset (or any other information sources) is disallowed during test. we simulate low-resource settings on the large hel-laswag and snli datasets by downsampling these to 2k and 3k training samples respectively; the other data sets are either already low-resource or have a low-resource component. dataset details are provided in appendix a. ## analysis and discussion we now analyze g-daug c 's performance, focusing on winogrande where g-daug c offers the most benefit. we first identify several factors that affect performance, and then present evidence that g-daug c works by transferring knowledge from the pretrained generator to the task model. ## conclusion we introduced g-daug c , a novel data augmentation framework to generate synthetic training data, preserving quality and diversity. we demonstrate that g-daug c is effective on multiple commonsense reasoning benchmarks, with improvements on in-distribution performance, as well as robustness against perturbed evaluation sets and challenge sets. our analysis shows that g-daug c tends to perform better in low-resource settings and that our data selection strategies are important for performance. future work might explore more sophisticated methods to enhance quality and diversity of generated training data, including having humans-in-the-loop for relabeling.