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Erschienen in:
Improving Chinese Semantic Role Labeling with English Proposition Bank Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

A Hierarchical LSTM Model for Joint Tasks

verfasst von : Qianrong Zhou, Liyun Wen, Xiaojie Wang, Long Ma, Yue Wang

Erschienen in: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data

Verlag: Springer International Publishing

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Abstract

Previous work has shown that joint modeling of two Natural Language Processing (NLP) tasks are effective for achieving better performances for both tasks. Lots of task-specific joint models are proposed. This paper proposes a Hierarchical Long Short-Term Memory (HLSTM) model and some its variants for modeling two tasks jointly. The models are flexible for modeling different types of combinations of tasks. It avoids task-specific feature engineering. Besides the enabling of correlation information between tasks, our models take the hierarchical relations between two tasks into consideration, which is not discussed in previous work. Experimental results show that our models outperform strong baselines in three different types of task combination. While both correlation information and hierarchical relations between two tasks are helpful to improve performances for both tasks, the models especially boost performance of tasks on the top of the hierarchical structures.

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Vorheriges Kapitel News Abridgement Algorithm Based on Word Alignment and Syntactic Parsing
Nächstes Kapitel Enhancing Neural Disfluency Detection with Hand-Crafted Features
Fußnoten
2
Sentences with ‘request’ intent are not included, since there is always no slot values in those sentences.
 
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Metadaten
Titel
A Hierarchical LSTM Model for Joint Tasks
verfasst von
Qianrong Zhou
Liyun Wen
Xiaojie Wang
Long Ma
Yue Wang
Copyright-Jahr
2016
Buch
Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data

Print ISBN: 978-3-319-47673-5

Electronic ISBN: 978-3-319-47674-2

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-47674-2_27