2019 EMNLP EMNLP 2019

Learning the Extraction Order of Multiple Relational Facts in a Sentence with Reinforcement Learning

Abstract

AbstractThe multiple relation extraction task tries to extract all relational facts from a sentence. Existing works didn’t consider the extraction order of relational facts in a sentence. In this paper we argue that the extraction order is important in this task. To take the extraction order into consideration, we apply the reinforcement learning into a sequence-to-sequence model. The proposed model could generate relational facts freely. Widely conducted experiments on two public datasets demonstrate the efficacy of the proposed method.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Machine Learning and Reinforcement Learning
🧭 Keyword Pioneer — multi-relation extraction
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio