# Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning

arXiv: https://arxiv.org/abs/1603.07954 · HTML: https://mlsanity.com/paper/1603.07954/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2016.

## Built on by (most cited first, 16 of 16)

- [WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents](https://mlsanity.com/paper/2207.01206/) (2022)
- [Quark: Controllable Text Generation with Reinforced Unlearning](https://mlsanity.com/paper/2205.13636/) (2022)
- [Decontextualization: Making Sentences Stand-Alone](https://mlsanity.com/paper/2102.05169/) (2021)
- [Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction](https://mlsanity.com/paper/2109.06415/) (2021)
- [Survey on reinforcement learning for language processing](https://mlsanity.com/paper/2104.05565/) (2021)
- [Boosting Search Engines with Interactive Agents](https://mlsanity.com/paper/2109.00527/) (2021)
- [A Survey on Deep Reinforcement Learning for Data Processing and Analytics](https://mlsanity.com/paper/2108.04526/) (2021)
- [Syntactic and Semantic-driven Learning for Open Information Extraction](https://mlsanity.com/paper/2103.03448/) (2021)
- [A Coarse to Fine Question Answering System based on Reinforcement Learning](https://mlsanity.com/paper/2106.00257/) (2021)
- [Query Rewriting for Effective Misinformation Discovery](https://mlsanity.com/paper/2210.07467/) (2022)
- [Successor Features for Efficient Multisubject Controlled Text Generation](https://mlsanity.com/paper/2311.04921/) (2023)
- [Adaptive Reinforcement Learning Planning: Harnessing Large Language Models for Complex Information Extraction](https://mlsanity.com/paper/2406.11455/) (2024)
- [Feudal Reinforcement Learning by Reading Manuals](https://mlsanity.com/paper/2110.06477/) (2021)
- [Gradient Imitation Reinforcement Learning for General Low-Resource Information Extraction](https://mlsanity.com/paper/2211.06014/) (2022)
- [Adaptive Ordered Information Extraction with Deep Reinforcement Learning](https://mlsanity.com/paper/2306.10787/) (2023)
- [Data Acquisition for Improving Model Fairness using Reinforcement Learning](https://mlsanity.com/paper/2412.03009/) (2024)

## Abstract (arXiv)

Most successful information extraction systems operate with access to a large collection of documents. In this work, we explore the task of acquiring and incorporating external evidence to improve extraction accuracy in domains where the amount of training data is scarce. This process entails issuing search queries, extraction from new sources and reconciliation of extracted values, which are repeated until sufficient evidence is collected. We approach the problem using a reinforcement learning framework where our model learns to select optimal actions based on contextual information. We employ a deep Q-network, trained to optimize a reward function that reflects extraction accuracy while penalizing extra effort. Our experiments on two databases -- of shooting incidents, and food adulteration cases -- demonstrate that our system significantly outperforms traditional extractors and a competitive meta-classifier baseline.