# Learning with Weak Supervision for Email Intent Detection

arXiv: https://arxiv.org/abs/2005.13084 · HTML: https://mlsanity.com/paper/2005.13084/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2020.

## Built on by (most cited first, 17 of 17)

- [WRENCH: A Comprehensive Benchmark for Weak Supervision](https://mlsanity.com/paper/2109.11377/) (2021)
- [Weakly-supervised Text Classification Based on Keyword Graph](https://mlsanity.com/paper/2110.02591/) (2021)
- [TELEClass: Taxonomy Enrichment and LLM-Enhanced Hierarchical Text Classification with Minimal Supervision](https://mlsanity.com/paper/2403.00165/) (2024)
- [Goldilocks: Just-Right Tuning of BERT for Technology-Assisted Review](https://mlsanity.com/paper/2105.01044/) (2021)
- [Knodle: Modular Weakly Supervised Learning with PyTorch](https://mlsanity.com/paper/2104.11557/) (2021)
- [PromptDA: Label-guided Data Augmentation for Prompt-based Few-shot Learners](https://mlsanity.com/paper/2205.09229/) (2022)
- [PIEClass: Weakly-Supervised Text Classification with Prompting and Noise-Robust Iterative Ensemble Training](https://mlsanity.com/paper/2305.13723/) (2023)
- [MEGClass: Extremely Weakly Supervised Text Classification via Mutually-Enhancing Text Granularities](https://mlsanity.com/paper/2304.01969/) (2023)
- [LabelAId: Just-in-time AI Interventions for Improving Human Labeling Quality and Domain Knowledge in Crowdsourcing Systems](https://mlsanity.com/paper/2403.09810/) (2024)
- [XAI-CLASS: Explanation-Enhanced Text Classification with Extremely Weak Supervision](https://mlsanity.com/paper/2311.00189/) (2023)
- [Binary Classification with Positive Labeling Sources](https://mlsanity.com/paper/2208.01704/) (2022)
- [WALNUT: A Benchmark on Semi-weakly Supervised Learning for Natural Language Understanding](https://mlsanity.com/paper/2108.12603/) (2021)
- [Adapting Task-Oriented Dialogue Models for Email Conversations](https://mlsanity.com/paper/2208.09439/) (2022)
- [Uncertainty-Aware Reward-based Deep Reinforcement Learning for Intent Analysis of Social Media Information](https://mlsanity.com/paper/2302.10195/) (2023)
- [MetaXT: Meta Cross-Task Transfer between Disparate Label Spaces](https://mlsanity.com/paper/2109.04240/) (2021)
- [Classifying Emails into Human vs Machine Category](https://mlsanity.com/paper/2112.07742/) (2021)
- [TEAMMix: Taxonomy Enrichment Augmentation and Minority-augmented Mixing Strategy for LLM-enhanced Weak-Supervised Hierarchical Text Classification](https://mlsanity.com/paper/2608.11044/) (2026)

## Abstract (arXiv)

Email remains one of the most frequently used means of online communication. People spend a significant amount of time every day on emails to exchange information, manage tasks and schedule events. Previous work has studied different ways for improving email productivity by prioritizing emails, suggesting automatic replies or identifying intents to recommend appropriate actions. The problem has been mostly posed as a supervised learning problem where models of different complexities were proposed to classify an email message into a predefined taxonomy of intents or classes. The need for labeled data has always been one of the largest bottlenecks in training supervised models. This is especially the case for many real-world tasks, such as email intent classification, where large scale annotated examples are either hard to acquire or unavailable due to privacy or data access constraints. Email users often take actions in response to intents expressed in an email (e.g., setting up a meeting in response to an email with a scheduling request). Such actions can be inferred from user interaction logs. In this paper, we propose to leverage user actions as a source of weak supervision, in addition to a limited set of annotated examples, to detect intents in emails. We develop an end-to-end robust deep neural network model for email intent identification that leverages both clean annotated data and noisy weak supervision along with a self-paced learning mechanism. Extensive experiments on three different intent detection tasks show that our approach can effectively leverage the weakly supervised data to improve intent detection in emails.