Auto-tagging of Short Conversational Sentences using Transformer Methods
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Abstract (arXiv)
The problem of categorizing short speech sentences according to their semantic features with high accuracy is a subject studied in natural language processing. In this study, a data set created with samples classified in 46 different categories was used. Examples consist of sentences taken from chat conversations between a company's customer representatives and the company's website visitors. The primary purpose is to automatically tag questions and requests from visitors in the most accurate way for 46 predetermined categories for use in a chat application to generate meaningful answers to the questions asked by the website visitors. For this, different BERT models and one GPT-2 model, pre-trained in Turkish, were preferred. The classification performances of the relevant models were analyzed in detail and reported accordingly.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 7 of its 15 references that are in the map. First posted on arXiv 3 Jun 2021.
Builds on
7 of 15 references.
References · 7
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
- 2020Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlationcited by 420
- 2019How to Fine-Tune BERT for Text Classification?cited by 409
- 2020Load What You Need: Smaller Versions of Multilingual BERTcited by 32
- 2019Is Multilingual BERT Fluent in Language Generation?cited by 28
- 2021Using GPT-2 to Create Synthetic Data to Improve the Prediction Performance of NLP Machine Learning Classification Modelscited by 2
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1.
2 papers.