PROMINET : Prototype-based Multi-View Network for Interpretable Email Response Prediction (2023)

Abstract: Email is a widely used tool for business communication, and email marketing has emerged as a cost-effective strategy for enterprises. While previous studies have examined factors affecting email marketing performance, limited research has focused on understanding email response behavior by considering email content and metadata. This study proposes a Prototype-based Multi-view Network (PROMINET) that incorporates semantic and structural information from email data. By utilizing prototype learning, the PROMINET model generates latent exemplars, enabling interpretable email response prediction. The model maps learned semantic and structural exemplars to observed samples in the training data at different levels of granularity, such as document, sentence, or phrase. The approach is evaluated on two real-world email datasets: the Enron corpus and an in-house Email Marketing corpus. Experimental results demonstrate that the PROMINET model outperforms baseline models, achieving a ~3% improvement in F1 score on both datasets. Additionally, the model provides interpretability through prototypes at different granularity levels while maintaining comparable performance to non-interpretable models. The learned prototypes also show potential for generating suggestions to enhance email text editing and improve the likelihood of effective email responses. This research contributes to enhancing sender-receiver communication and customer engagement in email interactions.

## introduction with the ever-increasing volume of emails being exchanged daily, email communication remains a cornerstone of business interactions and an effective means of content distribution. as the primary communication tool for organizations and individuals alike, email marketing has maintained its popularity over the years, evolving and expanding alongside advancements in technology. this form of marketing enables businesses to tailor targeted messages to customers based on their preferences, leveraging the quick, easy, and cost-effective nature of email communication. in this context, predicting customer response behavior in email marketing campaigns becomes crucial for optimizing customer-product engagements and enhancing communication efficiency between senders and recipients. consider the example email shown in figure 1 , where various factors such as the email's contents (subject and body) and the recipient's organization, can influence the likelihood of receiving a response. therefore, understanding the impact of these factors and their correlation with email response behavior is paramount. @xcite has shown that a single word can make a substantial difference in how a text is interpreted. this insight applies to our email response prediction task, making it essential to address this challenge. the likelihood of an email receiving a response can be influenced by various factors, including the use of power words or phrases, the persuasiveness of the text, and alignment with client preferences. given the sensitivity of words or phrases in our task, we need methods to extract both the structural and semantic information from email text to develop an effective prediction model. recently, there have been efforts to t o : john@<recipient_org_name>.com from: callie@<sender_org_name>.com subject: confirming you know 05/01/2020, fri., 09:04 am hi john, <@xmath0. we look forward to you discovering new ways to work, manage risk, optimize cost, and maintain customer satisfaction during this time. stay safe and well, john. best regards, callie email: [email] phone: [phone_no] study different explanation techniques for text clas-202 sification. these methods typically fall into two categories: post-hoc explanation methods @xcite and self-explaining approaches @xcite . post-hoc explanations use an additional explanatory model to provide explanations after making predictions, while self-explaining approaches generate explanations simultaneously with the prediction. however, post-hoc explanations may not accurately reveal the reasoning process of the original model @xcite , making it preferable to build models with inherent interpretability. in this work, we propose prominet, a novel interpretable email response prediction model that integrates semantic and structural information from email data. prominet utilizes prototype learning, a form of case-based reasoning, to make predictions based on similarities to representative examples (prototypes) in the training data. unlike existing prototype-based architectures, prominet provides explanations from multiple perspectives: semantic (using transformer-based models) and structural (using graph-based dependency parsing with gnn). by leveraging a multibranch network, prominet offers holistic explanations at different levels, including documentlevel, sentence-level, and phrase-level prototypes. we conduct quantitative analyses and ablation studies using two real-world email datasets: the enron corpus and the in-house email marketing corpus. our prominet model achieves superior performance and offers explanations that simulate potential edits, resulting in improved response rates. ## problem setup we tackle the interpretable email response prediction problem as a self-explainable binary classifica-tion task. given a training set d with email texts x i and binary response labels y @xmath1, 1}, our goal is to predict the likelihood of receiving a response while providing insights into the decision process. the labels indicate whether an email received a response (1) or not (0), which could include clicks, views, or replies. to enhance interpretability, we learn latent prototypes at the document, sentence, and phrase levels, mapping them to representative observations in the training set. these prototypes serve as classification references and analogical explanations for the model's decisions. ## methodology in this section, we introduce prominet model, that incorporates multi-view representations and prototype layers to develop a self-explainable email response prediction model. our architectural choices prioritize two key factors: accuracy and interpretability. to ensure accurate email response predictions, our model leverages features derived from the email subject, body, and recipient information. it does so by employing a multiview architecture that captures the interplay between different factors. the model extracts both structural and semantic information to comprehend the valuable cues pertaining to email persuasiveness and engagement. moreover, our model is designed to be interpretable, offering insights into decision-making at various levels. using the information from the multi-view representations, the model achieves interpretability through granular latent prototypes that serve as explanations for predictions. by considering both accuracy and interpretability, the model aims to strike a balance between making accurate predictions and providing transparent reasoning. in our prominet model, we incorporate two main views, namely the semantic view and the structural view, to achieve our goal. we acquire embeddings at the document, sentence, and phrase-level by employing different components described in the subsequent subsections. ## conclusion in this study, we introduced prominet, a prototype-based multi-view network that incorporates semantic and structural information from email data for interpretable email response prediction. prominet compared inputs to representative instances (prototypes) in the latent space to make predictions and offers prototypical explanations at the document, sentence, and phrase levels for enhanced human understanding. the evaluation on real-world email datasets demonstrates that prominet outperforms baseline models, achiev-ing a significant improvement of approximately 3% in f 1 score on both the enron corpus and the email marketing corpus. our research contributes to enhancing sender-receiver communication and customer engagement in email interactions, filling a gap in understanding by considering email content and metadata. future research directions involve addressing limitations such as time and historical interactions, handling unseen scenarios, improving interpretability, and balancing personalized content with prototypical information. these advancements will further propel the usage ai technqiues in email marketing and communication.