ST ra TA : Self-Training with Task Augmentation for Better Few-shot Learning (2021)

Abstract: Despite their recent successes in tackling many NLP tasks, large-scale pre-trained language models do not perform as well in few-shot settings where only a handful of training examples are available. To address this shortcoming, we propose STraTA, which stands for Self-Training with Task Augmentation, an approach that builds on two key ideas for effective leverage of unlabeled data. First, STraTA uses task augmentation, a novel technique that synthesizes a large amount of data for auxiliary-task fine-tuning from target-task unlabeled texts. Second, STraTA performs self-training by further fine-tuning the strong base model created by task augmentation on a broad distribution of pseudo-labeled data. Our experiments demonstrate that STraTA can substantially improve sample efficiency across 12 few-shot benchmarks. Remarkably, on the SST-2 sentiment dataset, STraTA, with only 8 training examples per class, achieves comparable results to standard fine-tuning with 67K training examples. Our analyses reveal that task augmentation and self-training are both complementary and independently effective.

## introduction recent advances in nlp demonstrate the effectiveness of applying large-scale pretrained language models to downstream tasks @xcite @xcite @xcite @xcite . while these models have achieved state-of-the-art results on many nlp benchmarks, they struggle when given limited training data. for instance, @xcite find that bert is prone to degenerate performance on small datasets. while enormous language models like gpt-3 @xcite exhibit the ability to solve a new task from only a few examples without work done as a student researcher at google brain. any fine-tuning, their performance still lags far behind state-of-the-art fine-tuning results. manually annotating large amounts of training data will likely improve performance but can also be prohibitively expensive to obtain for many tasks and domains. in this paper, we propose strata, an approach that combines two complementary methods, self-training and task augmentation, to effectively leverage unlabeled data, which is comparatively cheaper to obtain. 1 at a high level, task augmentation exploits unlabeled text from the domain of a given target task to simulate a large amount of in-domain training data for the auxiliary task of natural language inference (nli), which is then used to train a given model before applying it to the target task. to achieve this, we first build an nli data generator by fine-tuning a pre-trained generative language model on the mnli data set @xcite in a text-to-text format. then, given a target task (e.g., sentiment analysis) with unlabeled ## task augmentation labeled data is often expensive and timeconsuming to obtain, which motivates approaches that learn from both labeled and unlabeled data. more formally, assume we are given a target task t with a labeled data set and an unlabeled data set u @xmath0. the unlabeled data u t can be created artificially by removing the ground-truth labels y from l t (as in our main experiments), or it can come from additional unlabeled texts from the target domain or from related datasets/domains (see section 5). our methods, task augmentation and self-training, take advantage of the unlabeled data u t to maximize performance on the target task t , even when the number of labeled examples m is small (e.g., @xmath1. in this section, we first present a framework and implementation for task augmentation, which uses natural language inference (nli) as an auxiliary (intermediate) training task to improve downstream performance. ## experiments we perform experiments across 12 different nlp datasets and three different data regimes (including a few-shot setting). task augmentation consistently improves over prior fine-tuning approaches in all three regimes, and the combination of self-training and task augmentation, strata, results in higher performance and lower variance than competing approaches when given only 8 labeled examples per class from each dataset. ## analysis of few-shot learning results having established the effectiveness of both task augmentation and self-training in the few-shot setting, we conduct a series of analysis experiments 16 while @xcite report results for lm-bff and efl across 5 random data subsets using a fixed set of seeds, @xcite tried 10 seeds for each of their 5 random data subsets and report the mean of the top 3 seeds. to be more comparable to @xcite , we report the mean of our top 3 random seeds in ## limitations & conclusion task augmentation and self-training provide complementary ways to leverage task-specific unlabeled data for improved downstream performance. while task augmentation utilizes unlabeled texts to synthesize a large amount of in-domain data for an auxiliary training task, self-training uses a model's predictions on unlabeled examples to improve the model itself. when combining these methods in strata, we are able to substantially improve sample efficiency across 12 nlp benchmark datasets. that said, each method has its own limitations. while our implementation uses nli as an auxiliary task in task augmentation, there are target tasks for which nli may not be helpful (e.g., on grammatical acceptability judgments, as shown in @xcite ). additionally, other auxiliary tasks may increase improvements (e.g., qnli benefits more from qa tasks @xcite ). we leave exploration of other auxiliary tasks to future work. finally, our self-training algorithm (like prior approaches) assumes access to task-specific unlabeled examples, which might be non-trivial to acquire for some applications.