Repository logo
 

Semi-supervised multitask learning for sequence labeling

Published version
Peer-reviewed

Loading...
Thumbnail Image

Type

Conference Object

Change log

Authors

Rei, M 

Abstract

We propose a sequence labeling framework with a secondary training objective, learning to predict surrounding words for every word in the dataset. This language modeling objective incentivises the system to learn general-purpose patterns of semantic and syntactic composition, which are also useful for improving accuracy on different sequence labeling tasks. The architecture was evaluated on a range of datasets, covering the tasks of error detection in learner texts, named entity recognition, chunking and POS-tagging. The novel language modeling objective provided consistent performance improvements on every benchmark, without requiring any additional annotated or unannotated data.

Description

Keywords

Journal Title

ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)

Conference Name

Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Journal ISSN

Volume Title

1

Publisher

Association for Computational Linguistics
Sponsorship
Cambridge Assessment (unknown)