Multitask Learning for Sequence Labeling Tasks

TL;DR

This paper introduces a multitask learning approach for sequence labeling that trains multiple models simultaneously, each focusing on a primary label sequence, and sharing parameters to improve performance.

Contribution

It proposes a novel multitask learning framework with explicit parameter sharing for sequence labeling tasks involving multiple label sequences.

Findings

Significantly outperforms state-of-the-art methods

Effective learning transfer among models

Applicable to multiple sequence labeling applications

Abstract

In this paper, we present a learning method for sequence labeling tasks in which each example sequence has multiple label sequences. Our method learns multiple models, one model for each label sequence. Each model computes the joint probability of all label sequences given the example sequence. Although each model considers all label sequences, its primary focus is only one label sequence, and therefore, each model becomes a task-specific model, for the task belonging to that primary label. Such multiple models are learned {\it simultaneously} by facilitating the learning transfer among models through {\it explicit parameter sharing}. We experiment the proposed method on two applications and show that our method significantly outperforms the state-of-the-art method.