Mutual Reinforcement Effects in Japanese Sentence Classification and Named Entity Recognition Tasks

TL;DR

This paper investigates the mutual reinforcement between sentence classification and named entity recognition in Japanese, proposing a multi-task framework that improves accuracy and demonstrates the benefits of integrated learning.

Contribution

It introduces the SCNM multi-task approach with a novel Sentence-to-Label Generation framework and a constraint mechanism, advancing joint information extraction methods.

Findings

SC accuracy increased by 1.13 points with the approach

NER accuracy increased by 1.06 points with the approach

The framework outperforms baseline methods in few-shot learning scenarios

Abstract

Information extraction(IE) is a crucial subfield within natural language processing. However, for the traditionally segmented approach to sentence classification and Named Entity Recognition, the intricate interactions between these individual subtasks remain largely uninvestigated. In this study, we propose an integrative analysis, converging sentence classification with Named Entity Recognition, with the objective to unveil and comprehend the mutual reinforcement effect within these two information extraction subtasks. To achieve this, we introduce a Sentence Classification and Named Entity Recognition Multi-task (SCNM) approach that combines Sentence Classification (SC) and Named Entity Recognition (NER). We develop a Sentence-to-Label Generation (SLG) framework for SCNM and construct a Wikipedia dataset containing both SC and NER. Using a format converter, we unify input formats and employ a generative model to generate SC-labels, NER-labels, and associated text segments. We propose a Constraint Mechanism (CM) to improve generated format accuracy. Our results show SC accuracy increased by 1.13 points and NER by 1.06 points in SCNM compared to standalone tasks, with CM raising format accuracy from 63.61 to 100. The findings indicate mutual reinforcement effects between SC and NER, and integration enhances both tasks' performance. We additionally implemented the SLG framework on single SC task. It yielded superior accuracies compared to the baseline on two distinct Japanese SC datasets. Notably, in the experiment of few-shot learning, SLG framework shows much better performance than fine-tune method. These empirical findings contribute additional evidence to affirm the efficacy of the SLG framework.