Attentive Task-Agnostic Meta-Learning for Few-Shot Text Classification
Current deep learning based text classification methods are limited by their ability to achieve fast learning and generalization when the data is scarce. We address this problem by integrating a meta-learning procedure that uses the knowledge learned across many tasks as an inductive bias towards better natural language understanding. Inspired by the Model-Agnostic Meta-Learning framework (MAML), we introduce the Attentive Task-Agnostic Meta-Learning (ATAML) algorithm for text classification. The proposed ATAML is designed to encourage task-agnostic representation learning by way of task-agnostic parameterization and facilitate task-specific adaptation via attention mechanisms. We provide evidence to show that the attention mechanism in ATAML has a synergistic effect on learning performance. Our experimental results reveal that, for few-shot text classification tasks, gradient-based meta-learning approaches ourperform popular transfer learning methods. In comparisons with models trained from random initialization, pretrained models and meta trained MAML, our proposed ATAML method generalizes better on single-label and multi-label classification tasks in miniRCV1 and miniReuters-21578 datasets.
 This paper presents a meta learning approach for few-shot text classification, where task-specific parameters are used to compute a context-dependent weighted sum of hidden representations for a word sequence and intermediate representations of words are obtained by applying shared model parameters. The proposed meta learning architecture, namely ATAML, consistently outperforms baselines in terms of 1-shot classification tasks and these results demonstrate that the use of task-specific attention in ATAML has some positive impact on few-shot learning problems. The performance of ATAML on 5-shot classification, by contrast, is similar to its baseline, i.e., MAML. I couldn’t find in the manuscript the reason (or explanation) why the performance gain of ATAML over MAML gets smaller if we provide more examples per class. It would be also interesting to check the performance of both algorithms on 10-shot classification. This paper has limited its focus on meta learning for few-shot text classification according to the title and experimental setup, but the authors do not properly define the task itself.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Attentive Task-Agnostic Meta-Learning for Few-Shot Text Classification
Current deep learning based text classification methods are limited by their ability to achieve fast learning and generalization when the data is scarce. We address this problem by integrating a meta-learning procedure that uses the knowledge learned across many tasks as an inductive bias towards better natural language understanding. Inspired by the Model-Agnostic Meta-Learning framework (MAML), we introduce the Attentive Task-Agnostic Meta-Learning (ATAML) algorithm for text classification. The proposed ATAML is designed to encourage task-agnostic representation learning by way of task-agnostic parameterization and facilitate task-specific adaptation via attention mechanisms. We provide evidence to show that the attention mechanism in ATAML has a synergistic effect on learning performance. Our experimental results reveal that, for few-shot text classification tasks, gradient-based meta-learning approaches ourperform popular transfer learning methods. In comparisons with models trained from random initialization, pretrained models and meta trained MAML, our proposed ATAML method generalizes better on single-label and multi-label classification tasks in miniRCV1 and miniReuters-21578 datasets.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 This paper presents a meta learning approach for few-shot text classification, where task-specific parameters are used to compute a context-dependent weighted sum of hidden representations for a word sequence and intermediate representations of words are obtained by applying shared model parameters. The proposed meta learning architecture, namely ATAML, consistently outperforms baselines in terms of 1-shot classification tasks and these results demonstrate that the use of task-specific attention in ATAML has some positive impact on few-shot learning problems. The performance of ATAML on 5-shot classification, by contrast, is similar to its baseline, i.e., MAML. I couldn’t find in the manuscript the reason (or explanation) why the performance gain of ATAML over MAML gets smaller if we provide more examples per class. It would be also interesting to check the performance of both algorithms on 10-shot classification. This paper has limited its focus on meta learning for few-shot text classification according to the title and experimental setup, but the authors do not properly define the task itself.
assistant
 This paper presents a meta learning approach for few-shot text classification, where task-specific parameters are used to compute a context-dependent weighted sum of hidden representations for a word sequence and intermediate representations of words are obtained by applying shared model parameters. The proposed meta learning architecture, namely ATAML, consistently outperforms baselines in terms of 1-shot classification tasks and these results demonstrate that the use of task-specific attention in ATAML has some positive impact on few-shot learning problems. The performance of ATAML on 5-shot classification, by contrast, is similar to its baseline, i.e., MAML. I couldn’t find in the manuscript the reason (or explanation) why the performance gain of ATAML over MAML gets smaller if we provide more examples per class. It would be also interesting to check the performance of both algorithms on 10-shot classification. This paper has limited its focus on meta learning for few-shot text classification according to the title and experimental setup, but the authors do not properly define the task itself.