Learning sentence embeddings with auxiliary tasks for cross-domain sentiment classification

Learning sentence embeddings with auxiliary tasks for cross-domain sentiment classification

In this paper, we study cross-domain sentiment classification with neural network architectures. We borrow the idea from Structural Correspondence Learning and use two auxiliary tasks to help induce a sentence embedding that supposedly works well across domains for sentiment classification. We also...

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Bibliographic Details
Main Authors: YU, Jianfei, JIANG, Jing
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2016
Subjects:
Benchmark datasets
Cross-domain
Joint modeling
Sentiment classification
State-of-the-art methods
Databases and Information Systems
Software Engineering
Online Access:https://ink.library.smu.edu.sg/sis_research/3437
https://ink.library.smu.edu.sg/context/sis_research/article/4438/viewcontent/emnlp2016__2_.pdf
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Institution: Singapore Management University
Language: English
Description
Summary:In this paper, we study cross-domain sentiment classification with neural network architectures. We borrow the idea from Structural Correspondence Learning and use two auxiliary tasks to help induce a sentence embedding that supposedly works well across domains for sentiment classification. We also propose to jointly learn this sentence embedding together with the sentiment classifier itself. Experiment results demonstrate that our proposed joint model outperforms several state-of-the-art methods on five benchmark datasets.

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