Auto-tagging of Short Conversational Sentences using Transformer Methods
This addresses the problem of improving customer service efficiency in chat applications by enabling accurate response generation, but it is incremental as it applies existing transformer methods to a specific domain and dataset.
The study tackled automatic tagging of short conversational sentences into 46 categories for a chat application, using pre-trained Turkish BERT and GPT-2 models, and reported detailed classification performances.
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.