Auto-tagging of Short Conversational Sentences using Transformer Methods
Abstract
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.
Keywords
Cite
@article{arxiv.2106.01735,
title = {Auto-tagging of Short Conversational Sentences using Transformer Methods},
author = {D. Emre Taşar and Şükrü Ozan and Umut Özdil and M. Fatih Akca and Oğuzhan Ölmez and Semih Gülüm and Seçilay Kutal and Ceren Belhan},
journal= {arXiv preprint arXiv:2106.01735},
year = {2021}
}
Comments
in Turkish language