Smart Reply Suggestion for an Email in Multiple Languages – Data and Metho

Abstract

Smart Reply Suggestion (SRS) is a novel end-to-end system that refers toward the method of suggesting the three short email responses. These responses are themselves complete short email replies, suggested in diverse form for (monolingual) email i.e. English. In literature, mostly the model is specifically designed for monolingual emails like English. There is a paucity of state-of-the-art techniques that handle both monolingual and multilingual email at a time. Moreover, there is a paucity of benchmark corpus in multiple languages-based emails. This research has tried to overcome the limitations of an earlier study of the SRS system by proposing a novel method for the SRS system which handle both the monolingual and multilingual email at a time. In this research, we treat this problem as a multilabel text classification problem. we developed the " (ML-SRS) Email corpus collected from academia, consisting of three different languages including English, Roman Urdu and a combination of Roman Urdu and English emails (text) and developed the list of smart response prepared to annotate that dataset manually the inter-annotator agreement calculated and then standardized the dataset in .CSV format. For our proposed SRS system, we considered two approaches. In the first approach, we considered content-based n-gram approach at word and character level with the combination of multi-label classifier i.e. One Vs Rest, Label Powerset, with eight classical machine learning classifiers i.e. Linear SVC, Logistic Regression etc. In the second approach, we consider LSTM, CNN, GRU, Bi GRU, Bi-LSTM models. Both approaches are evaluated on Micro Precision, Micro Recall, Micro F1 score and Hamming loss. However, Label Powerset with the combination of Linear SVC performed well as compared to others