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        <title>Text Emotion Distribution Learning from Small Sample: A Meta-Learning Approach</title>
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        <namePart type="given">Zhenjie</namePart>
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        <namePart type="given">Xiaojuan</namePart>
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            <namePart type="given">Jing</namePart>
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    <abstract>Text emotion distribution learning (EDL) aims to develop models that can predict the intensity values of a sentence across a set of emotion categories. Existing methods based on supervised learning require a large amount of well-labelled training data, which is difficult to obtain due to inconsistent perception of fine-grained emotion intensity. In this paper, we propose a meta-learning approach to learn text emotion distributions from a small sample. Specifically, we propose to learn low-rank sentence embeddings by tensor decomposition to capture their contextual semantic similarity, and use K-nearest neighbors (KNNs) of each sentence in the embedding space to generate sample clusters. We then train a meta-learner that can adapt to new data with only a few training samples on the clusters, and further fit the meta-learner on KNNs of a testing sample for EDL. In this way, we effectively augment the learning ability of a model on the small sample. To demonstrate the performance, we compare the proposed approach with state-of-the-art EDL methods on a widely used EDL dataset: SemEval 2007 Task 14 (Strapparava and Mihalcea, 2007). Results show the superiority of our method on small-sample emotion distribution learning.</abstract>
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%0 Conference Proceedings
%T Text Emotion Distribution Learning from Small Sample: A Meta-Learning Approach
%A Zhao, Zhenjie
%A Ma, Xiaojuan
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F zhao-ma-2019-text
%X Text emotion distribution learning (EDL) aims to develop models that can predict the intensity values of a sentence across a set of emotion categories. Existing methods based on supervised learning require a large amount of well-labelled training data, which is difficult to obtain due to inconsistent perception of fine-grained emotion intensity. In this paper, we propose a meta-learning approach to learn text emotion distributions from a small sample. Specifically, we propose to learn low-rank sentence embeddings by tensor decomposition to capture their contextual semantic similarity, and use K-nearest neighbors (KNNs) of each sentence in the embedding space to generate sample clusters. We then train a meta-learner that can adapt to new data with only a few training samples on the clusters, and further fit the meta-learner on KNNs of a testing sample for EDL. In this way, we effectively augment the learning ability of a model on the small sample. To demonstrate the performance, we compare the proposed approach with state-of-the-art EDL methods on a widely used EDL dataset: SemEval 2007 Task 14 (Strapparava and Mihalcea, 2007). Results show the superiority of our method on small-sample emotion distribution learning.
%R 10.18653/v1/D19-1408
%U https://aclanthology.org/D19-1408
%U https://doi.org/10.18653/v1/D19-1408
%P 3957-3967

Markdown (Informal)

[Text Emotion Distribution Learning from Small Sample: A Meta-Learning Approach](https://aclanthology.org/D19-1408) (Zhao & Ma, EMNLP-IJCNLP 2019)

• Text Emotion Distribution Learning from Small Sample: A Meta-Learning Approach (Zhao & Ma, EMNLP-IJCNLP 2019)

ACL

• Zhenjie Zhao and Xiaojuan Ma. 2019. Text Emotion Distribution Learning from Small Sample: A Meta-Learning Approach. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3957–3967, Hong Kong, China. Association for Computational Linguistics.