Adversarial Weakly Supervised Domain Adaptation for Few Shot Sentiment Analysis

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Taher Seyyed Ehsan; SHAMSFARD MEHRNOUSH

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Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2021 | Issue:7

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Title

Adversarial Weakly Supervised Domain Adaptation for Few Shot Sentiment Analysis

Author(s)

Taher Seyyed Ehsan | SHAMSFARD MEHRNOUSH

Keywords

Adversarial Training

Weak Supervision

Domain Adaptation

Few Shot Learning

Persian Sentiment Analysis

Low Resource Languages

Abstract

The ability of deep neural networks to generate state-of-the-art results on many NLP problems has been apparent to everyone for some years now. However, when there is not enough labeled data or the test dataset has domain shift, these networks face many challenges and results are getting worse. In this article, we present a method for adapting the domain from formal to colloquial (in sentiment classification). Our method uses two approaches, Adversarial Training and Weak Supervision, and only needs a few shots of labeled data. In the first stage, we label a crawled dataset (containing colloquial and formal sentences) with weakly supervised sentiment tags using a sentiment vocabulary network. Then we fine-tune a pre-trained model with Adversarial Training on this weak dataset to generate domain-independent representations. In the last stage, we train the above fine-tuned neural network with just 50 samples of data (formal domain) and test it on colloquial. Experimental results show that our method outperforms the state-of-the-art model (Pars BERT) on the same data with 15% higher F1 measure.

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APA: Copy

Taher, Seyyed Ehsan, & SHAMSFARD, MEHRNOUSH. (2021). Adversarial Weakly Supervised Domain Adaptation for Few Shot Sentiment Analysis. INTERNATIONAL CONFERENCE ON WEB RESEARCH. SID. https://sid.ir/paper/949434/en

Vancouver: Copy

Taher Seyyed Ehsan, SHAMSFARD MEHRNOUSH. Adversarial Weakly Supervised Domain Adaptation for Few Shot Sentiment Analysis. 2021. Available from: https://sid.ir/paper/949434/en

IEEE: Copy

Seyyed Ehsan Taher, and MEHRNOUSH SHAMSFARD, “Adversarial Weakly Supervised Domain Adaptation for Few Shot Sentiment Analysis,” presented at the INTERNATIONAL CONFERENCE ON WEB RESEARCH. 2021, [Online]. Available: https://sid.ir/paper/949434/en

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