Few-Shot Learning for Intent Classification Using Pre-Trained BERT Model

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Kanani Mohammad Amin; AMINIAN MAHDI

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Seminar Paper

Paper Information

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

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Title

Few-Shot Learning for Intent Classification Using Pre-Trained BERT Model

Author(s)

Kanani Mohammad Amin | AMINIAN MAHDI

Keywords

Natural Language Processing

Natural Language Understanding

Intent Classification

Conversational Agents

Deep Learning

Machine Learning

Abstract

Intent Classification is one of the important tasks in Natural Language Understanding which aims to classify queries based on their intent, goal, or purpose which is implied in the content. However, the problem in this task is the lack of data labeled by a human agent. This problem leads to weakness in the generalization of models, especially when the models face rare words. Using pre-trained models can be useful in the generalization of language representation. BERT pre-trained language model which has been currently published has made a major impact in the Natural Language Processing field. This model which is trained on unlabeled large-scale corpora, with fine-tuning could achieve state-of-art results in various Natural Language Processing tasks e. g. question answering and sentiment analysis. In this paper, we compared the BERT model with conventional Machine Learning models and shown that the BERT model has a better performance than conventional Machine Learning models in few-shot learning.

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APA: Copy

Kanani, Mohammad Amin, & AMINIAN, MAHDI. (2021). Few-Shot Learning for Intent Classification Using Pre-Trained BERT Model. INTERNATIONAL CONFERENCE ON WEB RESEARCH. SID. https://sid.ir/paper/949462/en

Vancouver: Copy

Kanani Mohammad Amin, AMINIAN MAHDI. Few-Shot Learning for Intent Classification Using Pre-Trained BERT Model. 2021. Available from: https://sid.ir/paper/949462/en

IEEE: Copy

Mohammad Amin Kanani, and MAHDI AMINIAN, “Few-Shot Learning for Intent Classification Using Pre-Trained BERT Model,” presented at the INTERNATIONAL CONFERENCE ON WEB RESEARCH. 2021, [Online]. Available: https://sid.ir/paper/949462/en

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