Sentiment Classification by Leveraging the Shared Knowledge from a Sequence of Domains

Lv, Guangyi; Wang, Shuai; Liu, Bing; Chen, Enhong; Zhang, Kun

doi:10.1007/978-3-030-18576-3_47

Cited by 40 publications

(31 citation statements)

References 34 publications

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“…Xia et al (2017) proposed a voting method but their method works on the same data from different time periods. Lv et al (2019) proposed a model using two networks, one for knowledge retention and one for feature learning. But it was shown to be weaker than .…”

Section: Related Workmentioning

confidence: 99%

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Using the Past Knowledge to Improve Sentiment Classification

Qin

Liu

2020

Findings of the Association for Computational Linguistics: EMNLP 2020

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This paper studies sentiment classification in the lifelong learning setting that incrementally learns a sequence of sentiment classification tasks. It proposes a new lifelong learning model (called L2PG) that can retain and selectively transfer the knowledge learned in the past to help learn the new task. A key innovation of this proposed model is a novel parameter-gate (p-gate) mechanism that regulates the flow or transfer of the previously learned knowledge to the new task. Specifically, it can selectively use the network parameters (which represent the retained knowledge gained from the previous tasks) to assist the learning of the new task t. Knowledge distillation is also employed in the process to preserve the past knowledge by approximating the network output at the state when task t − 1 was learned. Experimental results show that L2PG outperforms strong baselines, including even multiple task learning.

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Section: Related Workmentioning

confidence: 99%

“…LNB: LNB is similar to LSC but is able to improve the model of a previous task without retraining. The system in (Lv et al, 2019) is not compared as it performed poorer than LNB .…”

Section: Baselinesmentioning

confidence: 99%

Using the Past Knowledge to Improve Sentiment Classification

Qin

Liu

2020

Findings of the Association for Computational Linguistics: EMNLP 2020

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“…Xu et al (2020) proposed a continuous naïve Bayes learning framework for e-commerce product review sentiment classification by extending the parameter estimation mechanism in naïve Bayes. Lv et al (2019) introduced a deep learning method for lifelong sentiment classification by jointly training two networks, a feature learning network and a knowledge retention network. Such a method is the first lifelong sentiment classification method based on deep learning.…”

Section: Related Workmentioning

confidence: 99%

“…The first lifelong learning method for sentiment classification (called lifelong sentiment classification) was introduced in (Chen et al, 2015). Following Chen et al (2015), several other lifelong sentiment classification approaches were proposed respectively in (Xia et al, 2017;Lv et al, 2019;Xu et al, 2020). We will discuss these and other related work in the next section.…”

Section: Introductionmentioning

confidence: 99%

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Bayes-enhanced Lifelong Attention Networks for Sentiment Classification

Wang¹,

Wang²,

Mazumder³

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics

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The classic deep learning paradigm learns a model from the training data of a single task and the learned model is also tested on the same task. This paper studies the problem of learning a sequence of tasks (sentiment classification tasks in our case). After each sentiment classification task is learned, its knowledge is retained to help future task learning. Following this setting, we explore attention neural networks and propose a Bayes-enhanced Lifelong Attention Network (BLAN). The key idea is to exploit the generative parameters of naïve Bayes to learn attention knowledge. The learned knowledge from each task is stored in a knowledge base and later used to build lifelong attentions. The constructed lifelong attentions are then used to enhance the attention of the network to help new task learning. Experimental results on product reviews from Amazon.com show the effectiveness of the proposed model.

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Aspect Sentiment Classification Based on Sequence to Sequence Reinforced Learning

Chu

Tang

et al. 2019

Human Centered Computing

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Sentiment Classification by Leveraging the Shared Knowledge from a Sequence of Domains

Cited by 40 publications

References 34 publications

Using the Past Knowledge to Improve Sentiment Classification

Using the Past Knowledge to Improve Sentiment Classification

Bayes-enhanced Lifelong Attention Networks for Sentiment Classification

Aspect Sentiment Classification Based on Sequence to Sequence Reinforced Learning

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