A New Concept of Deep Reinforcement Learning based Augmented General Sequence Tagging System

TL;DR

This paper introduces a novel deep reinforcement learning augmented sequence tagging system that improves performance on SLU and NLU tasks, outperforming current state-of-the-art models on benchmark datasets.

Contribution

The paper presents a new system combining DNN and DRL components for sequence tagging, especially enhancing minority tag modeling, with demonstrated superior performance.

Findings

Outperforms state-of-the-art on ATIS by 1.9% F1 score.

Outperforms state-of-the-art on CoNLL-2003 by 1.4% F1 score.

Effective in modeling minority tags in sequence tagging tasks.

Abstract

In this paper, a new deep reinforcement learning based augmented general sequence tagging system is proposed. The new system contains two parts: a deep neural network (DNN) based sequence tagging model and a deep reinforcement learning (DRL) based augmented tagger. The augmented tagger helps improve system performance by modeling the data with minority tags. The new system is evaluated on SLU and NLU sequence tagging tasks using ATIS and CoNLL-2003 benchmark datasets, to demonstrate the new system's outstanding performance on general tagging tasks. Evaluated by F1 scores, it shows that the new system outperforms the current state-of-the-art model on ATIS dataset by 1.9% and that on CoNLL-2003 dataset by 1.4%.