Domain Adaptation of Transformer-Based Models using Unlabeled Data for Relevance and Polarity Classification of German Customer Feedback

• URL: http://arxiv.org/abs/2212.05764v1
• Date: Mon, 12 Dec 2022 08:32:28 GMT
• Title: Domain Adaptation of Transformer-Based Models using Unlabeled Data for Relevance and Polarity Classification of German Customer Feedback
• Authors: Ahmad Idrissi-Yaghir, Henning Sch\"afer, Nadja Bauer, Christoph M. Friedrich
• Abstract summary: This work explores how efficient transformer-based models are when working with a German customer feedback dataset. The experimental results show that transformer-based models can reach significant improvements compared to a fastText baseline.
• Score: 1.2999413717930817
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding customer feedback is becoming a necessity for companies to identify problems and improve their products and services. Text classification and sentiment analysis can play a major role in analyzing this data by using a variety of machine and deep learning approaches. In this work, different transformer-based models are utilized to explore how efficient these models are when working with a German customer feedback dataset. In addition, these pre-trained models are further analyzed to determine if adapting them to a specific domain using unlabeled data can yield better results than off-the-shelf pre-trained models. To evaluate the models, two downstream tasks from the GermEval 2017 are considered. The experimental results show that transformer-based models can reach significant improvements compared to a fastText baseline and outperform the published scores and previous models. For the subtask Relevance Classification, the best models achieve a micro-averaged $F1$-Score of 96.1 % on the first test set and 95.9 % on the second one, and a score of 85.1 % and 85.3 % for the subtask Polarity Classification.

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