Improving Meta-learning for Low-resource Text Classification and Generation via Memory Imitation (2022)

Abstract: Building models of natural language processing (NLP) is challenging in low-resource scenarios where limited data are available. Optimization-based meta-learning algorithms achieve promising results in low-resource scenarios by adapting a well-generalized model initialization to handle new tasks. Nonetheless, these approaches suffer from the memorization overfitting issue, where the model tends to memorize the meta-training tasks while ignoring support sets when adapting to new tasks. To address this issue, we propose a memory imitation meta-learning (MemIML) method that enhances the model’s reliance on support sets for task adaptation. Specifically, we introduce a task-specific memory module to store support set information and construct an imitation module to force query sets to imitate the behaviors of support sets stored in the memory. A theoretical analysis is provided to prove the effectiveness of our method, and empirical results also demonstrate that our method outperforms competitive baselines on both text classification and generation tasks.

## introduction building natural language processing (nlp) models in low-resource scenarios is of great importance in practical applications because labeled data are scarce. meta-learning-based methods @xcite have been commonly used in such scenarios owing to their fast adaptation ability. notable successes have been achieved by metalearning on low-resource nlp tasks, such as multidomain sentiment classification @xcite and personalized dialogue generation @xcite @xcite . among different meta-learning approaches @xcite , optimization-based ap- * corresponding author proaches have been widely used in various lowresource nlp scenarios @xcite @xcite because they are model-agnostic and easily applicable. concretely, optimization-based meta-learning algorithms aim to learn a well-generalized global model initialization θ that can quickly adapt to new tasks within a few steps of gradient updates. in the meta-training process, we first train θ on a support set (i.e., a few training samples of a new task i) to obtain task-specific parameters θ i . then, we optimize θ based on the performance of θ i on a query set (i.e., another set of samples in task i). despite its effectiveness, optimization-based meta-learning algorithms usually suffer from the memorization overfitting issue foot_0 @xcite , where the learned model tends to solve all the meta-training tasks by memorization, rather than learning how to quickly adapt from one task to another via support sets. this is acceptable for training process, but results in poor generalization on the meta-testing sets, because the memorized model does not have knowledge of those tasks and does not know how to utilize the base learner to learn new tasks. hence, this issue hinders the model from capturing task-specific characteristics from support sets and thus prevents the model from adapting to distinct new tasks @xcite . for instance, in personalized dialogue generation, this implies that the dialog model cannot adapt to individual users based on short conversation histories and hence fails to generate personalized responses. several works have been proposed to tackle the memorization overfitting issue for regression and image classification tasks. some studies try to explicitly regularize the model parameters @xcite , but this restricts the complexity of model initialization and reduces the model capacity. another line of research integrates samples from support sets into the corresponding query sets via data augmentation @xcite . however, data augmentation on textual data may result in noisy labels or distribution shifts, which impairs the model performance @xcite . in this paper, we address the memorization overfitting issue by enhancing the model's dependence on support sets when learning the model initialization, which forces the model to better leverage information from support sets. as an analogy, consider a young investor who has the ability to adapt to new circumstances rapidly but little memory of learned experiences, and an old investor who is experienced but refuses to be flexible. our idea is to make the young investor adaptive to the various situations when he assesses his benefits so that he can not only take advantage of the old one's experience but also learn from the old investor how to leverage the learned experience. in this paper, the young investor stands for a standard meta-learning algorithm (e.g., maml), which is prone to memorization overfitting, and the old investor is a memory module we integrate into the method, carrying information of support sets. specifically, we propose a memory-imitation meta-learning (memiml) method that forces query set predictions to depend on their corresponding support sets by dynamically imitating behaviors of the latter. we therefore, introduce a memory module and an imitation module to enhance such dependence. the memory module is task-specific, storing representative information of support sets. the imitation module assists in predicting samples of query sets by dynamically imitating the memory construction. in this way, the model has to access the support set by memory imitation each time it makes a prediction on a query-set sample, hence it's no longer feasible for the model to memorize all meta tasks. the contributions of this work are: ## preliminaries we first formulate model-agnostic meta-learning (maml) @xcite . specifically, denote the base model used in maml as f θ and assume each task t i sampled from a task distribution p(t ) associates with a dataset ## methodology to alleviate the memorization overfitting issue in meta-learning, we propose memiml, which includes a memory module and an imitation module on the grounds of a base model. the memory module is task-specific, recording the mapping behaviors between inputs and outputs of support sets for each task. the imitation module is shared across tasks and predicts values for each query-set sample by dynamically imitating the memory construction. the acquired support set information leveraged by the imitation module augments the model initialization learning, enhancing the dependence of the model's task adaptation on support sets. fig. 1 shows our model architecture. ## experiments and analysis experiments on personalized dialogue generation and multi-domain sentiment classification verify our model on text generation and classification, respectively, where we use persona-chat and arsc datasets. ## conclusion in this paper, we tackle the memorization overfitting problem of meta-learning for text classification and generation applications. we propose memiml to enhance the dependence of the model on the support sets for task adaptation. memiml introduces a memory module storing the information of support sets, and propose an imitation module to better leverage the support set information by imitating the behaviors of the memory. both empirical and theoretical results demonstrate that our method memiml effectively alleviates the memorization overfitting problem.