Lifelong Learning Memory Networks for Aspect Sentiment Classification

Lv, Guangyi; Mazumder, Sahisnu; Fei, Geli; Liu, Bing

doi:10.1109/bigdata.2018.8622304

Cited by 44 publications

(15 citation statements)

References 24 publications

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“…Shu et al (2017) used LL for aspect extraction, which is a different problem. Wang et al (2018) used LL for ASC, but improved only the new task and did not deal with CF. Existing CL systems SRK (Lv et al, 2019), KAN (Ke et al, 2020b) and L2PG (Qin et al, 2020) are for document sentiment classification, but not ASC.…”

Section: Continual Learning Existing Work Has Mainly Focused On Dealing With Catastrophic Forgetting (Cf)mentioning

confidence: 99%

Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Tasks

Zixuan¹,

Xu²,

Liu³

2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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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. 1

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Section: Continual Learning Existing Work Has Mainly Focused On Dealing With Catastrophic Forgetting (Cf)mentioning

confidence: 99%

Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Tasks

Zixuan¹,

Xu²,

Liu³

2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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“…L2PG (Qin et al, 2020) uses a neural network but improves only the new task learning for DSC. Wang et al (2018) worked on ASC, but since they improve only the new task learning, they did not deal with CF. Each task uses a separate network.…”

Section: Related Workmentioning

confidence: 99%

CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks

Zixuan¹,

Liu²,

Xu³

et al. 2021

Preprint

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This paper studies continual learning (CL) of a sequence of aspect sentiment classification (ASC) tasks in a particular CL setting called domain incremental learning (DIL). Each task is from a different domain or product. The DIL setting is particularly suited to ASC because in testing the system needs not know the task/domain to which the test data belongs. To our knowledge, this setting has not been studied before for ASC. This paper proposes a novel model called CLASSIC. The key novelty is a contrastive continual learning method that enables both knowledge transfer across tasks and knowledge distillation from old tasks to the new task, which eliminates the need for task ids in testing. Experimental results show the high effectiveness of CLASSIC. 1 * Work was done prior to joining Amazon.

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“…Also related is the existing research about domain and context dependent sentiment. First, despite the fact that several researchers have studied context dependent sentiment words, which are based on sentences and topic/aspect context (Wilson et al, 2005;Ding et al, 2008;Choi and Cardie, 2008;Wu and Wen, 2010;Jijkoun et al, 2010;Lu et al, 2011;Zhao et al, 2012;Kessler and Schütze, 2012;Teng et al, 2016;Wang et al, 2016Wang et al, , 2018aLi et al, 2018a), our work is based on domains. Second, while the studies on transfer learning or domain adaptation for sentiment analysis deal with domain information (Bhatt et al, 2015;Yu and Jiang, 2016;Li et al, 2018b), our work does not lie in this direction.…”

Section: Related Workmentioning

confidence: 99%

Detecting Domain Polarity-Changes of Words in a Sentiment Lexicon

Wang¹,

Lv²,

Mazumder³

et al. 2021

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Sentiment lexicons are instrumental for sentiment analysis. One can use a set of sentiment words provided in a sentiment lexicon and a lexicon-based classifier to perform sentiment analysis. One major issue with this approach is that many sentiment words (from the lexicon) are domain dependent. That is, they may be positive in some domains but negative in some others. We refer to this problem as domain polarity-changes of words from a sentiment lexicon. Detecting such words and correcting their sentiment for an application domain is very important. In this paper, we propose a graph-based technique to tackle this problem. Experimental results show its effectiveness on multiple datasets from different domains.

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Lifelong Learning Memory Networks for Aspect Sentiment Classification

Cited by 44 publications

References 24 publications

Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Tasks

Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Tasks

CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks

Detecting Domain Polarity-Changes of Words in a Sentiment Lexicon

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