Smart Auto-completion in Live Chat Utilizing the Power of T5

Abstract: Auto-completion is a task that requires an algorithm to give suggestions for completing sentences. Specifically, the history of live chat and the words already typed by the agents are provided to the algorithm for outputting the suggestions to finish the sentences. This study aimed to investigate if the above task can be handled by fine-tuning a pre-trained T5 model on the target dataset. In this thesis, both an English and a Portuguese dataset were selected. Then, T5 and its multilingual version mT5were fine-tuned on the target datasets. The models were evaluated with different metrics (log perplexity, token level accuracy, and multi-word level accuracy), and the results are compared to those of the baseline methods. The results on these different metrics show that a method based on pre-trained T5 is a promising approach to handle the target task.