Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Grad2Task: Improved Few-shot Text Classification Using Gradients for
  Task Representation   Large pretrained language models (LMs) like BERT have improved performance in
many disparate natural language processing (NLP) tasks. However, fine tuning
such models requires a large number of training examples for each target task.
Simultaneously, many realistic NLP problems are "few shot", without a
sufficiently large training set. In this work, we propose a novel conditional
neural process-based approach for few-shot text classification that learns to
transfer from other diverse tasks with rich annotation. Our key idea is to
represent each task using gradient information from a base model and to train
an adaptation network that modulates a text classifier conditioned on the task
representation. While previous task-aware few-shot learners represent tasks by
input encoding, our novel task representation is more powerful, as the gradient
captures input-output relationships of a task. Experimental results show that
our approach outperforms traditional fine-tuning, sequential transfer learning,
and state-of-the-art meta learning approaches on a collection of diverse
few-shot tasks. We further conducted analysis and ablations to justify our
design choices.

4
The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
23
The categories of this paper are: solv-int
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an
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The categories of this paper are: cmp-lg
15
The categories of this paper are: nucl-ex