# ScopeIt: Scoping Task Relevant Sentences in Documents

arXiv: https://arxiv.org/abs/2003.04988 · HTML: https://mlsanity.com/paper/2003.04988/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Feb 2020.

## Built on by (most cited first, 3 of 3)

- [NaRLE: Natural Language Models using Reinforcement Learning with Emotion Feedback](https://mlsanity.com/paper/2110.02148/) (2021)
- [Utilizing coarse-grained data in low-data settings for event extraction](https://mlsanity.com/paper/2205.05468/) (2022)
- [Adapting Task-Oriented Dialogue Models for Email Conversations](https://mlsanity.com/paper/2208.09439/) (2022)

## Abstract (arXiv)

Intelligent assistants like Cortana, Siri, Alexa, and Google Assistant are trained to parse information when the conversation is synchronous and short; however, for email-based conversational agents, the communication is asynchronous, and often contains information irrelevant to the assistant. This makes it harder for the system to accurately detect intents, extract entities relevant to those intents and thereby perform the desired action. We present a neural model for scoping relevant information for the agent from a large query. We show that when used as a preprocessing step, the model improves performance of both intent detection and entity extraction tasks. We demonstrate the model's impact on Scheduler (Cortana is the persona of the agent, while Scheduler is the name of the service. We use them interchangeably in the context of this paper.) - a virtual conversational meeting scheduling assistant that interacts asynchronously with users through email. The model helps the entity extraction and intent detection tasks requisite by Scheduler achieve an average gain of 35% in precision without any drop in recall. Additionally, we demonstrate that the same approach can be used for component level analysis in large documents, such as signature block identification.