This paper studies continual learning (CL) for sentiment
classification (SC). In this setting, the CL system learns a sequence of
SC tasks incrementally in a neural network, where each task builds a
classifier to classify the sentiment of reviews of a particular product category or domain. Two natural questions are: Can the system transfer the
knowledge learned in the past from the previous tasks to the new task
to help it learn a better model for the new task? And, can old models
for previous tasks be improved in the process as well? This paper proposes a novel technique called KAN to achieve these objectives. KAN
can markedly improve the SC accuracy of both the new task and the old
tasks via forward and backward knowledge transfer. The effectiveness of
KAN is demonstrated through extensive experiments.
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Sequential mastery of multiple visual tasks: Networks naturally learn to learn and forget to forget
We explore the behavior of a standard convolutional
neural net in a continual-learning setting that introduces
visual classification tasks sequentially and requires the
net to master new tasks while preserving mastery of
previously learned tasks. This setting corresponds to
that which human learners face as they acquire domain
expertise serially, for example, as an individual studies
a textbook. Through simulations involving sequences of
ten related visual tasks, we find reason for optimism that
nets will scale well as they advance from having a single
skill to becoming multi-skill domain experts. We observe
two key phenomena. First, forward facilitation—the accelerated learning of task n+1 having learned n previous
tasks—grows with n. Second, backward interference—
the forgetting of the n previous tasks when learning task
n + 1—diminishes with n. Amplifying forward facilitation is the goal of research on metalearning, and
attenuating backward interference is the goal of research
on catastrophic forgetting. We find that both of these
goals are attained simply through broader exposure to a
domain.
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- Award ID(s):
- 1631428
- NSF-PAR ID:
- 10197704
- Date Published:
- Journal Name:
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition
- ISSN:
- 2332-564X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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This paper studies continual learning (CL) of a sequence of aspect sentiment classification (ASC) tasks. Although some CL techniques have been proposed for document sentiment classification, we are not aware of any CL work on ASC. A CL system that incrementally learns a sequence of ASC tasks should address the following two issues: (1) transfer knowledge learned from previous tasks to the new task to help it learn a better model, and (2) maintain the performance of the models for previous tasks so that they are not forgotten. This paper proposes a novel capsule network based model called B-CL to address these issues. B-CL markedly improves the ASC performance on both the new task and the old tasks via forward and backward knowledge transfer. The effectiveness of B-CL is demonstrated through extensive experiments.more » « less
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By learning a sequence of tasks continually, an agent in continual learning (CL) can improve the learning performance of both a new task and `old' tasks by leveraging the forward knowledge transfer and the backward knowledge transfer, respectively. However, most existing CL methods focus on addressing catastrophic forgetting in neural networks by minimizing the modification of the learnt model for old tasks. This inevitably limits the backward knowledge transfer from the new task to the old tasks, because judicious model updates could possibly improve the learning performance of the old tasks as well. To tackle this problem, we first theoretically analyze the conditions under which updating the learnt model of old tasks could be beneficial for CL and also lead to backward knowledge transfer, based on the gradient projection onto the input subspaces of old tasks. Building on the theoretical analysis, we next develop a ContinUal learning method with Backward knowlEdge tRansfer (CUBER), for a fixed capacity neural network without data replay. In particular, CUBER first characterizes the task correlation to identify the positively correlated old tasks in a layer-wise manner, and then selectively modifies the learnt model of the old tasks when learning the new task. Experimental studies show that CUBER can even achieve positive backward knowledge transfer on several existing CL benchmarks for the first time without data replay, where the related baselines still suffer from catastrophic forgetting (negative backward knowledge transfer). The superior performance of CUBER on the backward knowledge transfer also leads to higher accuracy accordingly.more » « less
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
