Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Generative Language Model for Few-shot Aspect-Based Sentiment Analysis   Sentiment analysis is an important task in natural language processing. In
recent works, pre-trained language models are often used to achieve
state-of-the-art results, especially when training data is scarce. It is common
to fine-tune on the downstream task, usually by adding task-specific layers on
top of the model. In this paper, we focus on aspect-based sentiment analysis,
which involves extracting aspect term, category, and predicting their
corresponding polarities. In particular, we are interested in few-shot
settings. We propose to reformulate the extraction and prediction tasks into
the sequence generation task, using a generative language model with
unidirectional attention (GPT2 is used unless stated otherwise). This way, the
model learns to accomplish the tasks via language generation without the need
of training task-specific layers. Our evaluation results on the single-task
polarity prediction show that our approach outperforms the previous
state-of-the-art (based on BERT) on average performance by a large margins in
few-shot and full-shot settings. More importantly, our generative approach
significantly reduces the model variance caused by low-resource data. We
further demonstrate that the proposed generative language model can handle
joint and multi-task settings, unlike previous work. We observe that the
proposed sequence generation method achieves further improved performances on
polarity prediction when the model is trained via joint and multi-task
settings. Further evaluation on similar sentiment analysis datasets, SST-2,
SST- and OOS intent detection validates the superiority and noise robustness of
generative language model in few-shot settings.

18
The categories of this paper are: econ
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
17
The categories of this paper are: q-fin
2
The categories of this paper are: math
9
The categories of this paper are: nlin
32
The categories of this paper are: atom-ph